% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

%book{Aho:72,
%    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
%}

%book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

%article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

%inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

%book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

%article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733}   ,
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

%article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

﻿% This file was created with Citavi 6.14.0.0

@proceedings{.06122023,
 year = {06122023},
 title = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9798400701924 },
 doi = {10.1145/3593013 }
}


@proceedings{.2017,
 year = {2017},
 title = {{Proceedings of the 26th International Conference on World Wide Web}},
 address = {Republic and Canton of Geneva, Switzerland},
 publisher = {{International World Wide Web Conferences Steering Committee}}
}


@proceedings{.2018,
 year = {2018},
 title = {{Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society}},
 address = {New York, NY, USA},
 publisher = {ACM}
}


@proceedings{.2019,
 year = {2019},
 title = {{ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
 publisher = {IEEE},
 isbn = {978-1-4799-8131-1 }
}


@proceedings{.2019b,
 year = {2019},
 title = {{ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
 publisher = {IEEE}
}


@proceedings{.2019c,
 year = {2019},
 title = {{Proceedings of the Conference on Fairness, Accountability, and Transparency}},
 address = {New York, NY, USA},
 publisher = {ACM}
}


@proceedings{.2019d,
 year = {2019},
 title = {{Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval}},
 address = {New York, NY, USA},
 publisher = {ACM}
}


@proceedings{.2020,
 year = {2020},
 title = {{12th ACM Conference on Web Science}},
 address = {New York, NY, USA},
 publisher = {ACM}
}


@proceedings{.2021,
 year = {2021},
 title = {{2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}},
 publisher = {IEEE},
 isbn = {978-1-6654-0191-3 }
}


@proceedings{.2021b,
 year = {2021},
 title = {{Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9781450383097},
 doi = {10.1145/3442188}
}


@proceedings{.2021c,
 year = {2021},
 title = {{Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}},
 address = {New York, NY, USA},
 publisher = {ACM}
}


@proceedings{.2021d,
 year = {2021},
 title = {{Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency}},
 address = {New York, NY, USA},
 publisher = {ACM}
}


@proceedings{.2022,
 year = {2022},
 title = {{2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)}},
 publisher = {IEEE}
}


@book{.2022b,
 year = {2022},
 title = {{Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}},
 publisher = {{Association for Computational Linguistics}}
}


@proceedings{.2022c,
 year = {2022}
}


@proceedings{.2023,
 year = {2023},
 publisher = {{Springer, Cham}}
}


@proceedings{.2023b,
 year = {2023},
 title = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9798400701924},
 doi = {10.1145/3593013}
}


@proceedings{.2023c,
 year = {2023},
 title = {{2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)}},
 publisher = {IEEE}
}


@proceedings{.2023d,
 year = {2023},
 title = {{2023 International Conference on Disruptive Technologies (ICDT)}},
 publisher = {IEEE}
}


@proceedings{.2023e,
 year = {2023},
 title = {{Findings of the Association for Computational Linguistics: ACL 2023}},
 address = {Toronto, Canada},
 publisher = {{Association for Computational Linguistics}}
}


@proceedings{.2023f,
 year = {2023},
 title = {{Findings of the Association for Computational Linguistics: ACL 2023}},
 address = {Toronto, Canada},
 publisher = {{Association for Computational Linguistics}}
}


@proceedings{.2023g,
 year = {2023},
 title = {{Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)}},
 address = {Toronto, Canada},
 publisher = {{Association for Computational Linguistics}}
}


@proceedings{.2023h,
 year = {2023},
 title = {{Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis}},
 address = {Toronto, Canada},
 publisher = {{Association for Computational Linguistics}}
}


@proceedings{.2023i,
 year = {2023},
 title = {{Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)}},
 address = {Toronto, Canada},
 publisher = {{Association for Computational Linguistics}}
}


@proceedings{.2023j,
 year = {2023},
 title = {{Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)}},
 address = {Toronto, Canada},
 publisher = {{Association for Computational Linguistics}}
}


@book{.2023k,
 year = {2023},
 title = {{Proceedings of the International Conference on Cognitive and Intelligent Computing}},
 publisher = {{Springer, Singapore}}
}


@article{.2023l,
 year = {2023},
 title = {{Response Sharing Platform}},
 journal = {{International Research Journal of Modernization in Engineering Technology and Science}},
 doi = {10.56726/IRJMETS40032}
}


@article{Abdelhakim.2023,
 abstract = {The fight against offensive speech on the Internet necessitates increased efforts from linguistic analysis and artificial intelligence perspectives to develop countermeasures and preventive methods. Reliable predictions can only be obtained if these methods are exposed to a representative sample of the domain or environment under consideration. Datasets serve as the foundation for significant developments in this field because they are the main means of obtaining appropriate instances that reveal the multiple and varied faces of the offensive speech phenomenon. In this sense, we present Ar-PuFi, a dataset of offensive speech towards Public Figures in the Arabian community. With 24,071 comments collected from TV interviews with Egyptian celebrities belonging to six domains of public interest, Ar-PuFi is currently the largest Arabic dataset in terms of its category and size. The examples were annotated by three native speakers over the course of two months and are provided with two-class and six-class annotations based on the presence or absence of explicit and implicit offensive content. We evaluated the performance of a diverse set of classification models employing several text representations of actual examples (e.g., N-gram, TF/IDF, AraVec, and fastText), and AraBERT achieved the baseline for the new dataset in both offensive detection and group classification. Additionally, we apply the Pointwise Mutual Information (PMI) technique to comments within the target domain in order to derive a lexicon of offensive terms associated with each domain of ArPuFi. We further explored whether active learning (AL) or meta-learning (ML) frameworks could be used to reduce the labeling effort required for our dataset without affecting prediction quality and found that, though AL can reduce the amount of data annotations by 10{\%} over the ML approach, neither approach requires less than about 70{\%} of the full dataset to achieve baseline performance. Finally, we took advantage of the availability of relevant datasets and conducted a cross-domain experiment to back up our claims not only about the uniqueness of our dataset but also about the difficulty of adapting Arabic dialects against one another.},
 author = {Abdelhakim, Mohamed and Liu, Bingquan and Sun, Chengie},
 year = {2023},
 title = {{Ar-PuFi: A Short-Text Dataset to Identify the Offensive Messages Towards Public Figures in the Arabian Community}},
 pages = {120888},
 issn = {09574174},
 journal = {{Expert Systems with Applications}},
 doi = {10.1016/j.eswa.2023.120888}
}


@inproceedings{Abercrombie.2023,
 abstract = {Much work in natural language processing (NLP) relies on human annotation. The majority of this implicitly assumes that annotator's labels are temporally stable, although the reality is that human judgements are rarely consistent over time. As a subjective annotation task, hate speech labels depend on annotator's emotional and moral reactions to the language used to convey the message. Studies in Cognitive Science reveal a `foreign language effect', whereby people take differing moral positions and perceive offensive phrases to be weaker in their second languages. Does this affect annotations as well? We conduct an experiment to investigate the impacts of (1) time and (2) different language conditions (English and German) on measurements of intra-annotator agreement in a hate speech labelling task. While we do not observe the expected lower stability in the different language condition, we find that overall agreement is significantly lower than is implicitly assumed in annotation tasks, which has important implications for dataset reproducibility in NLP.},
 author = {Abercrombie, Gavin and Hovy, Dirk and Prabhakaran, Vinodkumar},
 title = {{Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling}},
 url = {https://aclanthology.org/2023.law-1.10},
 pages = {96--103},
 publisher = {{Association for Computational Linguistics}},
 booktitle = {{Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)}},
 year = {2023},
 address = {Toronto, Canada}
}


@misc{Agarwal.2018,
 abstract = {We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.},
 author = {Agarwal, Alekh and Beygelzimer, Alina and Dud{\'i}k, Miroslav and Langford, John and Wallach, Hanna},
 date = {2018},
year = {2018},
 title = {{A Reductions Approach to Fair Classification}},
 keywords = {FOS: Computer and information sciences;Machine Learning (cs.LG)},
 publisher = {arXiv},
 doi = {10.48550/arXiv.1803.02453},
 file = {reductions approach to fair classifcation:Attachments/reductions approach to fair classifcation.pdf:application/pdf}
}


@inproceedings{Ahia.2023,
 abstract = {Model explanations that shed light on the model's predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include making them trustworthy and faithful to the model's predictions. In this work, we propose a novel framework for guiding model explanations by supervising them explicitly. To this end, our method, LEXplain, uses task-related lexicons to directly supervise model explanations. This approach consistently improves the model's explanations without sacrificing performance on the task, as we demonstrate on sentiment analysis and toxicity detection. Our analyses show that our method also demotes spurious correlations (i.e., with respect to African American English dialect) when performing the task, improving fairness.},
 author = {Ahia, Orevaoghene and Gonen, Hila and Balachandran, Vidhisha and Tsvetkov, Yulia and Smith, Noah A.},
 title = {{LEXPLAIN: Improving Model Explanations via Lexicon Supervision}},
 url = {https://aclanthology.org/2023.starsem-1.19},
 pages = {207--216},
 publisher = {{Association for Computational Linguistics}},
 booktitle = {{Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)}},
 year = {2023},
 address = {Toronto, Canada}
}


@article{Alessandro.2017,
 abstract = {Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.},
 author = {d'Alessandro, Brian and O'Neil, Cathy and LaGatta, Tom},
 year = {2017},
 title = {{Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification}},
 keywords = {Algorithms;Data Interpretation, Statistical;data science;disparate impact;ethics;Humans;Machine Learning},
 pages = {120--134},
 volume = {5},
 number = {2},
 journal = {{Big Data}},
 doi = {10.1089/big.2016.0048}
}


@article{Alharthi.2023,
 abstract = {Despite a body of research revolving around online abusive language, aiming at different

objectives such as detection, diffusion prediction, and mitigation, existing research has seldom looked at

factors motivating this behaviour. To further research in this direction, we investigate the motivations behind online abuse by looking at the characteristics of the targets of such abuse, i.e. is the abuse more prominent for specific characteristics of the targets? To enable target-oriented research into online abuse, we introduce the Online Abusive Attacks (OAA) dataset, the first benchmark dataset providing a holistic view of online abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations.

Further, we conduct an in-depth statistical analysis of online abuse centred around the targets' characteristics.

We identify two types of abusive attacks: those motivated by characteristics of the targets (identity-based attacks) and others (behavioural attacks). We find that online abusive attacks are predominantly motivated by the targets' identities (97{\%}), behavioural attacks accounting for a much smaller proportion (3{\%}). Abuse is also more likely to target users who are popular and have a verified status. Interestingly, an analysis of the user bios shows no clear indication that keywords used in the bios are likely to trigger abuse. Additionally, we also look at the frequency with which perpetrators perform online abusive attacks. Our analysis shows a large number of infrequent perpetrators, with only a few recurrent perpetrators. 

Findings from our study have important implications for the development of abusive language detection models that incorporate an awareness of the targets to improve their potential for prediction},
 author = {Alharthi, Raneem and Alharthi, Rajwa and Shekhar, Ravi and Zubiaga, Arkaitz},
 year = {2023},
 title = {{Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis}},
 pages = {64114--64127},
 volume = {11},
 issn = {2169-3536},
 journal = {{IEEE Access}},
 doi = {10.1109/access.2023.3289148},
 file = {Alharthi, Alharthi et al. 2023 - Target-Oriented Investigation of Online Abusive:Attachments/Alharthi, Alharthi et al. 2023 - Target-Oriented Investigation of Online Abusive.pdf:application/pdf}
}


@book{Almeida.2023,
 abstract = {This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra features in many downstream NLP tasks.},
 author = {Almeida, Felipe and Xex{\'e}o, Geraldo},
 year = {2023},
 title = {{Word Embeddings: A Survey}},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning;Statistics - Machine Learning},
 file = {Almeida, Xex{\'e}o 2023 - Word Embeddings:Attachments/Almeida, Xex{\'e}o 2023 - Word Embeddings.pdf:application/pdf}
}


@article{Antoci.2014,
 abstract = {The objective of this paper is to theoretically analyze how human interaction may evolve in a world characterized by the explosion of online networking and other Web-mediated ways of building and nurturing relationships. The analysis shows that online networking yields a storage mechanism through which any individual contribution---e.g. a blog post, a comment, or a photo---is stored within a particular network and ready for virtual access by each member who connects to the network. When someone provides feedback, for example by commenting on a note, or by replying to a message, the interaction is finalized. These interactions are asynchronous, i.e. they allow individuals to relate in different moments, whenever they have time to. When the social environment is poor of participation opportunities and/or the pressure on time increases (for example due to the need to increase the working time), the stock of information and ties stored in the Internet can help individuals to defend their sociability.},
 author = {Antoci, Angelo and Sabatini, Fabio and Sodini, Mauro},
 year = {2014},
 title = {{Bowling alone but tweeting together: the evolution of human interaction in the social networking era}},
 url = {https://link.springer.com/article/10.1007/s11135-013-9863-z?hc_location=ufi&error=cookies_not_supported&code=28dce466-e417-4a89-8080-afc90ccb9c41}   ,
 keywords = {general;Methodology of the Social Sciences;Social Sciences},
 pages = {1911--1927},
 volume = {48},
 number = {4},
 issn = {1573-7845},
 journal = {{Quality {\&} Quantity}},
 doi = {10.1007/s11135-013-9863-z},
 file = {Antoci, Sabatini et al. 2014 - Bowling alone but tweeting together:Attachments/Antoci, Sabatini et al. 2014 - Bowling alone but tweeting together.pdf:application/pdf}
}


@misc{Antypas.04.07.2023,
 abstract = {The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data creation processes contain their own biases, and models inherently learn from these dataset-specific biases. In this paper, we perform a large-scale cross-dataset comparison where we fine-tune language models on different hate speech detection datasets. This analysis shows how some datasets are more generalisable than others when used as training data. Crucially, our experiments show how combining hate speech detection datasets can contribute to the development of robust hate speech detection models. This robustness holds even when controlling by data size and compared with the best individual datasets.},
 author = {Antypas, Dimosthenis and Camacho-Collados, Jose},
 date = {04.07.2023},
year = {2023},
 title = {{Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical  Evaluation}},
 url = {http://arxiv.org/pdf/2307.01680v1},
 keywords = {Computer Science - Computation and Language}
}


@inproceedings{Arango.2019,
 abstract = {Hate speech is an important problem that is seriously affecting the dynamics and usefulness of online social communities. Large scale social platforms are currently investing important resources into automatically detecting and classifying hateful content, without much success. On the other hand, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only within specific datasets. In this work, we analyze this apparent contradiction between existing literature and actual applications. We study closely the experimental methodology used in prior work and their generalizability to other datasets. Our findings evidence methodological issues, as well as an important dataset bias. As a consequence, performance claims of the current state-of-the-art have become significantly overestimated. The problems that we have found are mostly related to data overfitting and sampling issues. We discuss the implications for current research and re-conduct experiments to give a more accurate picture of the current state-of-the art methods.},
 author = {Arango, Aym{\'e} and P{\'e}rez, Jorge and Poblete, Barbara},
 title = {{Hate Speech Detection is Not as Easy as You May Think}},
 publisher = {ACM},
 booktitle = {{Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval}},
 year = {2019},
 address = {New York, NY, USA},
 doi = {10.1145/3331184.3331262}
}


@proceedings{Araujo.2009,
 year = {2009},
 title = {{Pattern recognition and image analysis: 4th Iberian conference, IbPRIA 2009, P{\'o}voa de Varzim, Portugal, June 10 - 12, 2009 ; proceedings}},
 keywords = {Artificial Intelligence;Computer graphics;Computer Science;Computer vision;Optical pattern recognition;Text processing (Computer science},
 address = {Berlin and Heidelberg},
 volume = {5524},
 publisher = {Springer},
 isbn = {978-3-642-02172-5},
 series = {{Lecture Notes in Computer Science}},
 editor = {Araujo, Helder},
 institution = {IbPRIA},
 doi = {10.1007/978-3-642-02172-5}
}


@book{Artelt.2019,
 abstract = {Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instance of explanations are counterfactual explanations which provide an intuitive and useful explanations of machine learning models. In this survey we review model-specific methods for efficiently computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in literature so far.},
 author = {Artelt, Andr{\'e} and Hammer, Barbara},
 year = {2019},
 title = {{On the computation of counterfactual explanations -- A survey}},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Learning;Statistics - Machine Learning},
 file = {Artelt, Hammer 2019 - On the computation of counterfactual:Attachments/Artelt, Hammer 2019 - On the computation of counterfactual.pdf:application/pdf}
}


@misc{Attanasio.2022,
 abstract = {Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, fairness, and generalizability. E.g., neural hate speech detection models are strongly influenced by identity terms like gay, or women, resulting in false positives, severe unintended bias, and lower performance. Most mitigation techniques use lists of identity terms or samples from the target domain during training. However, this approach requires a-priori knowledge and introduces further bias if important terms are neglected. Instead, we propose a knowledge-free Entropy-based Attention Regularization (EAR) to discourage overfitting to training-specific terms. An additional objective function penalizes tokens with low self-attention entropy. We fine-tune BERT via EAR: the resulting model matches or exceeds state-of-the-art performance for hate speech classification and bias metrics on three benchmark corpora in English and Italian. EAR also reveals overfitting terms, i.e., terms most likely to induce bias, to help identify their effect on the model, task, and predictions.},
 author = {Attanasio, Giuseppe and Nozza, Debora and Hovy, Dirk and Baralis, Elena},
 date = {2022},
year = {2022},
 title = {{Entropy-based Attention Regularization Frees Unintended Bias Mitigation  from Lists}},
 url = {http://arxiv.org/pdf/2203.09192.pdf},
 keywords = {Computation and Language (cs.CL);Computer Science - Computation and Language},
 file = {Attanasio, Nozza et al. 17.03.2022 - Entropy-based Attention Regularization Frees Unintended:Attachments/Attanasio, Nozza et al. 17.03.2022 - Entropy-based Attention Regularization Frees Unintended.pdf:application/pdf}
}


@inproceedings{Badjatiya.2019,
 abstract = {With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to proactively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. 

In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. 

Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size $\sim$96k and a Twitter dataset of size $\sim$24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks.},
 author = {Badjatiya, Pinkesh and Gupta, Manish and Varma, Vasudeva},
 title = {{Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations}},
 pages = {49--59},
 publisher = {ACM},
 isbn = {9781450366748},
 editor = {Liu, Ling and White, Ryen},
 booktitle = {{The World Wide Web Conference}},
 year = {2019},
 address = {New York, NY, USA},
 doi = {10.1145/3308558.3313504},
 file = {Stereotypical{\_}Bias{\_}Removal{\_}for{\_}Hate{\_}Speech{\_}Detecti:Attachments/Stereotypical{\_}Bias{\_}Removal{\_}for{\_}Hate{\_}Speech{\_}Detecti.pdf:application/pdf}
}


@misc{Baktash.2023,
 abstract = {Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research article, we have discussed the features of GPT-4, its potential applications, and the challenges that it might face. We have also compared GPT-4 with its predecessor, GPT-3. GPT-4 has a larger model size (more than one trillion), better multilingual capabilities, improved contextual understanding, and reasoning capabilities than GPT-3. Some of the potential applications of GPT-4 include chatbots, personal assistants, language translation, text summarization, and question-answering. However, GPT-4 poses several challenges and limitations such as computational requirements, data requirements, and ethical concerns.},
 author = {Baktash, Jawid Ahmad and Dawodi, Mursal},
 date = {05.05.2023},
 title = {{Gpt-4: A Review on Advancements and Opportunities in Natural Language  Processing}},
 url = {http://arxiv.org/pdf/2305.03195},
 keywords = {Artificial Intelligence (cs.AI);Computation and Language (cs.CL);Computer Science - Artificial Intelligence;Computer Science - Computation and Language;Computer Science - Computers and Society;Computers and Society (cs.CY)},
 file = {Baktash, Dawodi 05.05.2023 - Gpt-4 A Review on Advancements:Attachments/Baktash, Dawodi 05.05.2023 - Gpt-4 A Review on Advancements.pdf:application/pdf}
}


@inproceedings{BallBurack.2021,
 abstract = {Automated systems for detecting harmful social media content are afflicted by a variety of biases, some of which originate in their training datasets. In particular, some systems have been shown to propagate racial dialect bias: they systematically classify content aligned with the African American English (AAE) dialect as harmful at a higher rate than content aligned with White English (WE). This perpetuates prejudice by silencing the Black community. Towards this problem we adapt and apply two existing bias mitigation approaches: preferential sampling pre-processing and adversarial debiasing in-processing. We analyse the impact of our interventions on model performance and propagated bias. We find that when bias mitigation is employed, a high degree of predictive accuracy is maintained relative to baseline, and in many cases bias against AAE in harmful tweet predictions is reduced. However, the specific effects of these interventions on bias and performance vary widely between dataset contexts. This variation suggests the unpredictability of autonomous harmful content detection outside of its development context. We argue that this, and the low performance of these systems at baseline, raise questions about the reliability and role of such systems in high-impact, real-world settings.},
 author = {Ball-Burack, Ari and Lee, Michelle Seng Ah and Cobbe, Jennifer and Singh, Jatinder},
 title = {{Differential Tweetment: Mitigating Racial Dialect Bias in Harmful Tweet Detection}},
 pages = {116--128},
 publisher = {ACM},
 isbn = {9781450383097},
 booktitle = {{Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2021},
 address = {New York, NY, USA},
 doi = {10.1145/3442188.3445875},
 file = {differential tweetment:Attachments/differential tweetment.pdf:application/pdf}
}


@inproceedings{Baumann.2023,
 abstract = {Nowadays, Machine Learning (ML) systems are widely used in various businesses and are increasingly being adopted to make decisions that can significantly impact people's lives. However, these decision-making systems rely on data-driven learning, which poses a risk of propagating the bias embedded in the data. Despite various attempts by the algorithmic fairness community to outline different types of bias in data and algorithms, there is still a limited understanding of how these biases relate to the fairness of ML-based decision-making systems. In addition, efforts to mitigate bias and unfairness are often agnostic to the specific type(s) of bias present in the data. This paper explores the nature of fundamental types of bias, discussing their relationship to moral and technical frameworks. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced throughout the entire modelling pipeline and possibly how to mitigate it. Our primary contribution is a framework for generating synthetic datasets with different forms of biases. We use our proposed synthetic data generator to perform experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. Furthermore, we provide initial insights into mitigating specific types of bias through post-processing techniques. The implementation of the synthetic data generator and experiments can be found at https://github.com/rcrupiISP/BiasOnDemand.},
 author = {Baumann, Joachim and Castelnovo, Alessandro and Crupi, Riccardo and Inverardi, Nicole and Regoli, Daniele},
 title = {{Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias}},
 pages = {1002--1013},
 publisher = {ACM},
 isbn = {9798400701924},
 booktitle = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3593013.3594058},
 file = {Bias on Demand:Attachments/Bias on Demand.pdf:application/pdf}
}


@inproceedings{Bergman.2023,
 author = {Bergman, A. Stevie and Hendricks, Lisa Anne and Rauh, Maribeth and Wu, Boxi and Agnew, William and Kunesch, Markus and Duan, Isabella and Gabriel, Iason and Isaac, William},
 title = {{Representation in AI Evaluations}},
 pages = {519--533},
 publisher = {ACM},
 isbn = {9798400701924},
 booktitle = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3593013.3594019}
}


@article{Beyens.2016,
 author = {Beyens, Ine and Frison, Eline and Eggermont, Steven},
 year = {2016},
 title = {{``I don't want to miss a thing'': Adolescents' fear of missing out and its relationship to adolescents' social needs, Facebook use, and Facebook related stress}},
 pages = {1--8},
 volume = {64},
 issn = {0747-5632},
 journal = {{Computers in Human Behavior}},
 doi = {10.1016/j.chb.2016.05.083},
 file = {Beyens, Frison et al. 2016 - I don't want to miss:Attachments/Beyens, Frison et al. 2016 - I don't want to miss.pdf:application/pdf}
}


@article{Bigoulaeva.2023,
 abstract = {The goal of hate speech detection is to filter negative online content aiming at certain groups of people. Due to the easy accessibility and multilinguality of social media platforms, it is crucial to protect everyone which requires building hate speech detection systems for a wide range of languages. However, the available labeled hate speech datasets are limited, making it difficult to build systems for many languages. In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages, while highlighting label issues across application scenarios, such as inconsistent label sets of corpora or differing hate speech definitions, which hinder the application of such methods. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply them to the target language, which lacks labeled examples, and show that good performance can be achieved. We then incorporate unlabeled target language data for further model improvements by bootstrapping labels using an ensemble of different model architectures. Furthermore, we investigate the issue of label imbalance in hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance. We test simple data undersampling and oversampling techniques and show their effectiveness.},
 author = {Bigoulaeva, Irina and Hangya, Viktor and Gurevych, Iryna and Fraser, Alexander},
 year = {2023},
 title = {{Label modification and bootstrapping for zero-shot cross-lingual hate speech detection}},
 url = {https://link.springer.com/article/10.1007/s10579-023-09637-4}  ,
 keywords = {Computational Linguistics;Computer Science;general;Language and Literature;Linguistics},
 pages = {1--32},
 issn = {1574-0218},
 journal = {{Language Resources and Evaluation}},
 doi = {10.1007/s10579-023-09637-4},
 file = {Bigoulaeva, Hangya et al. 2023 - Label modification and bootstrapping (2):Attachments/Bigoulaeva, Hangya et al. 2023 - Label modification and bootstrapping (2).pdf:application/pdf}
}


@article{Bilewicz.2020,
 abstract = {Exposure to derogatory language about immigrants and minority groups leads to political radicalization and deteriorates intergroup relations. This article addresses the psychological processes responsible for these effects as well as those involved in hate-speech proliferation in contemporary societies and discusses the factors that constrain its growth. We propose that frequent exposure to hate speech has severe effects on emotional, behavioral, and normative levels. Exposure to hate speech results in empathy being replaced by intergroup contempt as a dominant response to others---it is both a motivator and a consequence of derogatory language.

The increased presence of hate speech in one's environment creates a sense of a descriptive norm that allows outgroup derogation. This leads to the erosion of existing antidiscriminatory norms. Finally, through a process of desensitization, hate speech reduces people's ability to recognize the offensive character of such language.

Based on empirical evidence from the fields of social psychology and psychology of emotion and aggression, we propose a model that explains the described processes, and we trace the dynamics of this model using an agent-based modeling approach. We show that the mechanisms potentially effective in constraining hate-speech proliferation (empathy, norms) are eroded by hate speech itself. We argue that through basic psychological dynamics, societies become more accepting of derogatory language and less accepting of immigrants, as well as religious, ethnic, and sexual minorities.},
 author = {Bilewicz, Micha{\l} and Soral, Wiktor},
 year = {2020},
 title = {{Hate Speech Epidemic. The Dynamic Effects of Derogatory Language on Intergroup Relations and Political Radicalization}},
 pages = {3--33},
 volume = {41},
 number = {S1},
 issn = {0162-895X},
 journal = {{Political Psychology}},
 doi = {10.1111/pops.12670}
}


@inproceedings{Blodgett.2020,
 abstract = {acl 2020},
 author = {Blodgett, Su Lin and Barocas, Solon and {Daum{\'e} III}, Hal and Wallach, Hanna},
 title = {{Language (Technology) is Power: A Critical Survey of ``Bias'' in NLP}},
 pages = {5454--5476},
 publisher = {{Association for Computational Linguistics}},
 editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel},
 booktitle = {{Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}},
 year = {2020},
 address = {Stroudsburg, PA, USA},
 doi = {10.18653/v1/2020.acl-main.485},
 file = {Language (Technology) is Power A Critical Survey of ``Bias'' in NL:Attachments/Language (Technology) is Power A Critical Survey of ``Bias'' in NL.pdf:application/pdf}
}


@book{Bolukbasi.2016,
 abstract = {The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to {\textquotedbl}debias{\textquotedbl} the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.},
 author = {Bolukbasi, Tolga and Chang, Kai-Wei and Zou, James and Saligrama, Venkatesh and Kalai, Adam},
 year = {2016},
 title = {{Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word  Embeddings}},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computation and Language;Computer Science - Learning;Statistics - Machine Learning},
 file = {Bolukbasi, Chang et al. 2016 - Man is to Computer Programmer:Attachments/Bolukbasi, Chang et al. 2016 - Man is to Computer Programmer.pdf:application/pdf}
}


@misc{Bonaldi.2023,
 abstract = {Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-domain overfitting, resulting in models generating acceptable narratives only for hatred similar to training data, with little portability to other targets or to real-world toxic language. This paper introduces novel attention regularization methodologies to improve the generalization capabilities of PLMs for counter narratives generation. Overfitting to training-specific terms is then discouraged, resulting in more diverse and richer narratives. We experiment with two attention-based regularization techniques on a benchmark English dataset. Regularized models produce better counter narratives than state-of-the-art approaches in most cases, both in terms of automatic metrics and human evaluation, especially when hateful targets are not present in the training data. This work paves the way for better and more flexible counter-speech generation models, a task for which datasets are highly challenging to produce.},
 author = {Bonaldi, Helena and Attanasio, Giuseppe and Nozza, Debora and Guerini, Marco},
 date = {2023},
 title = {{Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation  via Attention Regularization}},
 url = {http://arxiv.org/pdf/2309.02311v1},
 keywords = {Computer Science - Computation and Language},
 file = {Bonaldi, Attanasio et al 05092023 - Weigh Your Own Words:Attachments/Bonaldi, Attanasio et al 05092023 - Weigh Your Own Words.pdf:application/pdf}
}


@inproceedings{Bruyne.2023,
 abstract = {The dominance of English is a well-known issue in NLP research. In this position paper, I turn to state-of-the-art psychological insights to explain why this problem is especially persistent in research on automatic emotion detection, and why the seemingly promising approach of using multilingual models to include lower-resourced languages might not be the desired solution. Instead, I campaign for the use of models that acknowledge linguistic and cultural differences in emotion conceptualization and verbalization. Moreover, I see much potential in NLP to better understand emotions and emotional language use across different languages.},
 author = {de Bruyne, Luna},
 title = {{The Paradox of Multilingual Emotion Detection}},
 url = {https://aclanthology.org/2023.wassa-1.40},
 pages = {458--466},
 publisher = {{Association for Computational Linguistics}},
 booktitle = {{Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis}},
 year = {2023},
 address = {Toronto, Canada}
}


@article{Buolamwini.2018,
 abstract = {Gender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationJoy Buolamwini,~Timnit GebruRecent studies demonstrate that machin...},
 author = {{Joy Buolamwini} and {Timnit Gebru}},
 year = {2018},
 title = {{Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification}},
 url = {http://proceedings.mlr.press/v81/buolamwini18a.html?mod=article_inline&ref=akusion-ci-shi-dai-bizinesumedeia},
 pages = {77--91},
 issn = {2640-3498},
 journal = {{Conference on Fairness, Accountability and Transparency}},
 file = {Joy Buolamwini, Timnit Gebru 2018 - Gender Shades:Attachments/Joy Buolamwini, Timnit Gebru 2018 - Gender Shades.pdf:application/pdf}
}


@proceedings{Burstein.2019,
 year = {2019},
 title = {{Proceedings of the 2019 Conference of the North}},
 address = {Stroudsburg, PA, USA},
 publisher = {{Association for Computational Linguistics}},
 editor = {Burstein, Jill and Doran, Christy and Solorio, Thamar},
 doi = {10.18653/v1/N19-1}
}


@book{Cabitza.2023,
 abstract = {Most Artificial Intelligence applications are based on supervised machinelearning (ML), which ultimately grounds on manually annotated data. Theannotation process is often performed in terms of a majority vote and this hasbeen proved to be often problematic, as highlighted by recent studies on theevaluation of ML models. In this article we describe and advocate for adifferent paradigm, which we call data perspectivism, which moves away fromtraditional gold standard datasets, towards the adoption of methods thatintegrate the opinions and perspectives of the human subjects involved in theknowledge representation step of ML processes. Drawing on previous works whichinspired our proposal we describe the potential of our proposal for not onlythe more subjective tasks (e.g. those related to human language) but also totasks commonly understood as objective (e.g. medical decision making), andpresent the main advantages of adopting a perspectivist stance in ML, as wellas possible disadvantages, and various ways in which such a stance can beimplemented in practice. Finally, we share a set of recommendations and outlinea research agenda to advance the perspectivist stance in ML.},
 author = {Cabitza, Federico and Campagner, Andrea and Basile, Valerio},
 year = {2023},
 title = {{Toward a Perspectivist Turn in Ground Truthing for Predictive Computing}},
 volume = {37},
 doi = {10.1609/aaai.v37i6.25840},
 file = {Cabitza, Campagner et al. 2023 - Toward a Perspectivist Turn:Attachments/Cabitza, Campagner et al. 2023 - Toward a Perspectivist Turn.pdf:application/pdf}
}


@misc{Cai.2022,
 abstract = {Hate speech detection is a common downstream application of natural language processing (NLP) in the real world. In spite of the increasing accuracy, current data-driven approaches could easily learn biases from the imbalanced data distributions originating from humans. The deployment of biased models could further enhance the existing social biases. But unlike handling tabular data, defining and mitigating biases in text classifiers, which deal with unstructured data, are more challenging. A popular solution for improving machine learning fairness in NLP is to conduct the debiasing process with a list of potentially discriminated words given by human annotators. In addition to suffering from the risks of overlooking the biased terms, exhaustively identifying bias with human annotators are unsustainable since discrimination is variable among different datasets and may evolve over time. To this end, we propose an automatic misuse detector (MiD) relying on an explanation method for detecting potential bias. And built upon that, an end-to-end debiasing framework with the proposed staged correction is designed for text classifiers without any external resources required.},
 author = {Cai, Yi and Zimek, Arthur and Wunder, Gerhard and Ntoutsi, Eirini},
 year = {2022},
 title = {{Power of Explanations: Towards automatic debiasing in hate speech  detection}},
 url = {http://arxiv.org/pdf/2209.09975v1},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning},
 file = {Cai, Zimek et al 07092022 - Power of Explanations:Attachments/Cai, Zimek et al 07092022 - Power of Explanations.pdf:application/pdf}
}


@book{Caselli.2021,
 abstract = {In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have collected and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the abuse-inclined version obtained by retraining with posts from the banned communities on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the generic pre-trained language model and its corresponding abusive language-inclined counterpart across the datasets, indicating that portability is affected by compatibility of the annotated phenomena.},
 author = {Caselli, Tommaso and Basile, Valerio and Mitrovi{\'c}, Jelena and Granitzer, Michael},
 year = {2021},
 title = {{HateBERT: Retraining BERT for Abusive Language Detection in English}},
 publisher = {Association for Computational Linguistics},
 keywords = {Computer Science - Computation and Language},
 file = {Caselli, Basile et al. 2021 - HateBERT Retraining BERT for Abusive:Attachments/Caselli, Basile et al. 2021 - HateBERT Retraining BERT for Abusive.pdf:application/pdf}
}


@article{Castelnovo.2021,
 abstract = {In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention in the scientific communities dealing with Artificial Intelligence. A plethora of different definitions of fairness in ML have been proposed, that consider d...},
 author = {Castelnovo, Alessandro and Crupi, Riccardo and Greco, Greta and Regoli, Daniele and Penco, Ilaria Giuseppina and Cosentini, Andrea Claudio},
 year = {2021},
 title = {{The Zoo of Fairness Metrics in Machine Learning}},
 url = {https://www.researchsquare.com/article/rs-1162350/latest},
 doi = {10.21203/rs.3.rs-1162350/v1},
 file = {Castelnovo, Crupi et al. 2021 - The Zoo of Fairness Metrics:Attachments/Castelnovo, Crupi et al. 2021 - The Zoo of Fairness Metrics.pdf:application/pdf}
}


@article{Chai.2022,
 abstract = {Fairness with Adaptive WeightsJunyi Chai,~Xiaoqian WangFairness is now an important issue in machine learning. There are arising concerns that automated...},
 author = {{Junyi Chai} and {Xiaoqian Wang}},
 year = {2022},
 title = {{Fairness with Adaptive Weights}},
 url = {https://proceedings.mlr.press/v162/chai22a.html},
 pages = {2853--2866},
 issn = {2640-3498},
 journal = {{International Conference on Machine Learning}},
 file = {Junyi Chai, Xiaoqian Wang 2022 - Fairness with Adaptive Weights:Attachments/Junyi Chai, Xiaoqian Wang 2022 - Fairness with Adaptive Weights.pdf:application/pdf}
}


@proceedings{Champin.2018,
 year = {2018},
 title = {{Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18}},
 address = {New York, New York, USA},
 publisher = {{ACM Press}},
 isbn = {9781450356398},
 editor = {Champin, Pierre-Antoine and Gandon, Fabien and Lalmas, Mounia and Ipeirotis, Panagiotis G.},
 doi = {10.1145/3178876}
}


@misc{Chaudhary.07.09.2021,
 abstract = {Online hate speech has caught everyone's attention from the news related to the COVID-19 pandemic, US elections, and worldwide protests. Online toxicity - an umbrella term for online hateful behavior, manifests itself in forms such as online hate speech. Hate speech is a deliberate attack directed towards an individual or a group motivated by the targeted entity's identity or opinions. The rising mass communication through social media further exacerbates the harmful consequences of online hate speech. While there has been significant research on hate-speech identification using Natural Language Processing (NLP), the work on utilizing NLP for prevention and intervention of online hate speech lacks relatively. This paper presents a holistic conceptual framework on hate-speech NLP countering methods along with a thorough survey on the current progress of NLP for countering online hate speech. It classifies the countering techniques based on their time of action, and identifies potential future research areas on this topic.},
 author = {Chaudhary, Mudit and Saxena, Chandni and Meng, Helen},
 date = {07.09.2021},
 title = {{Countering Online Hate Speech: An NLP Perspective}},
 url = {http://arxiv.org/pdf/2109.02941v1},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning},
 file = {Countering Online Hate Speech An NLP Perspective:Attachments/Countering Online Hate Speech An NLP Perspective.pdf:application/pdf}
}


@article{Chawla.2002,
 abstract = {An approach to the construction of classifiers from    imbalanced datasets is described. A dataset is imbalanced if the    classification categories are not approximately equally    represented. Often real-world data sets are predominately composed of    ``normal'' examples with only a small percentage of ``abnormal'' or    ``interesting'' examples. It is also the case that the cost of    misclassifying an abnormal (interesting) example as a normal example    is often much higher than the cost of the reverse    error. Under-sampling of the majority (normal) class has been proposed    as a good means of increasing the sensitivity of a classifier to the    minority class. This paper shows that a combination of our method of    over-sampling the minority (abnormal) class and under-sampling the    majority (normal) class can achieve better classifier performance (in    ROC space) than only under-sampling the majority class.  This paper    also shows that a combination of our method of over-sampling the    minority class and under-sampling the majority class can achieve    better classifier performance (in ROC space) than varying the loss    ratios in Ripper or class priors in Naive Bayes. Our method of    over-sampling the minority class involves creating synthetic minority    class examples.  Experiments are performed using C4.5, Ripper and a    Naive Bayes classifier. The method is evaluated using the area under    the Receiver Operating Characteristic curve (AUC) and the ROC convex    hull strategy.},
 author = {Chawla, N. V. and Bowyer, K. W. and Hall, L. O. and Kegelmeyer, W. P.},
 year = {2002},
 title = {{SMOTE: Synthetic Minority Over-sampling Technique}},
 url = {https://www.jair.org/index.php/jair/article/view/10302},
 pages = {321--357},
 volume = {16},
 issn = {1076-9757},
 journal = {{Journal of Artificial Intelligence Research}},
 doi = {10.1613/jair.953},
 file = {Chawla, Bowyer et al. 2002 - SMOTE Synthetic Minority Over-sampling Technique:Attachments/Chawla, Bowyer et al. 2002 - SMOTE Synthetic Minority Over-sampling Technique.pdf:application/pdf}
}


@misc{Chen.2022,
 abstract = {Software bias is an increasingly important operational concern for software engineers. We present a large-scale, comprehensive empirical study of 17 representative bias mitigation methods for Machine Learning (ML) classifiers, evaluated with 11 ML performance metrics (e.g., accuracy), 4 fairness metrics, and 20 types of fairness-performance trade-off assessment, applied to 8 widely-adopted software decision tasks. The empirical coverage is much more comprehensive, covering the largest numbers of bias mitigation methods, evaluation metrics, and fairness-performance trade-off measures compared to previous work on this important software property. We find that (1) the bias mitigation methods significantly decrease ML performance in 53{\%} of the studied scenarios (ranging between 42{\%}{\~{}}66{\%} according to different ML performance metrics); (2) the bias mitigation methods significantly improve fairness measured by the 4 used metrics in 46{\%} of all the scenarios (ranging between 24{\%}{\~{}}59{\%} according to different fairness metrics); (3) the bias mitigation methods even lead to decrease in both fairness and ML performance in 25{\%} of the scenarios; (4) the effectiveness of the bias mitigation methods depends on tasks, models, the choice of protected attributes, and the set of metrics used to assess fairness and ML performance; (5) there is no bias mitigation method that can achieve the best trade-off in all the scenarios. The best method that we find outperforms other methods in 30{\%} of the scenarios. Researchers and practitioners need to choose the bias mitigation method best suited to their intended application scenario(s).

Accepted by ACM Transactions on Software Engineering and Methodology (TOSEM 2023). Please include TOSEM in any citations},
 author = {Chen, Zhenpeng and Zhang, Jie M. and Sarro, Federica and Harman, Mark},
 date = {2022},
 title = {{A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers}},
 keywords = {Artificial Intelligence (cs.AI);FOS: Computer and information sciences;Software Engineering (cs.SE)},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2207.03277 }
}


@inproceedings{Chen.2023,
 abstract = {Despite the remarkable performances in various applications, machine learning (ML) models could potentially discriminate. They may result in biasness in decision-making, leading to an impact negatively on individuals and society. Recently, various methods have been developed to mitigate biasness and achieve significant performance. Attention mechanisms are a fundamental component of many state-of-the-art ML models and may potentially impact the fairness of ML models. However, how they explicitly influence fairness has yet to be thoroughly explored. In this paper, we investigate how different attention mechanisms affect the fairness of ML models, focusing on models used in Natural Language Processing (NLP) models. We evaluate the performance of fairness of several models with and without different attention mechanisms on widely used benchmark datasets. Our results indicate that the majority of attention mechanisms that have been assessed can improve the fairness performance of Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) in all three datasets regarding religious and gender-sensitive groups, however, with varying degrees of trade-offs in accuracy measures. Our findings highlight the possibility of fairness being affected by adopting specific attention mechanisms in machine learning models for certain datasets},
 author = {Chen, Shijing and Naseem, Usman and Razzak, Imran},
 title = {{Debunking Biases in Attention}},
 url = {https://aclanthology.org/2023.trustnlp-1.13},
 pages = {141--150},
 publisher = {{Association for Computational Linguistics}},
 booktitle = {{Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)}},
 year = {2023},
 address = {Toronto, Canada},
 file = {Chen, Naseem et al 2023 - Debunking Biases in Attention:Attachments/Chen, Naseem et al 2023 - Debunking Biases in Attention.pdf:application/pdf}
}

@inproceedings{Chen.2024,
 author = {Chen, Zhenpeng and Zhang, Jie M. and Sarro, Federica and Harman, Mark},
 title = {Fairness Improvement with Multiple Protected Attributes: How Far Are We?},
 pages = {1-13},
 publisher = {ACM},
 isbn = {9798400702174},
 editor = {Roychoudhury, Abhik and Paiva, Ana and Abreu, Rui and Storey, Margaret},
 booktitle = {Proceedings of the IEEE/ACM 46th International Conference on Software Engineering},
 year = {2024},
 address = {New York, NY, USA},
 doi = {10.1145/3597503.3639083}
}

@article{Morse.2022,
 author = {Morse, Lily and Teodorescu, Mike Horia M. and Awwad, Yazeed and Kane, Gerald C.},
 year = {2022},
 title = {Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning Algorithms},
 pages = {1083--1095},
 volume = {181},
 number = {4},
 issn = {0167-4544},
 journal = {Journal of Business Ethics},
 doi = {10.1007/s10551-021-04939-5},
 file = {Morse, Teodorescu et al. 2022 - Do the Ends Justify:Attachments/Morse, Teodorescu et al. 2022 - Do the Ends Justify.pdf:application/pdf}
}


@proceedings{Chua.2023,
 year = {2023},
 title = {{Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9781450394079 },
 editor = {Chua, Tat-Seng and Lauw, Hady and Si, Luo and Terzi, Evimaria and Tsaparas, Panayiotis},
 doi = {10.1145/3539597 }
}


@misc{Chuang.14.06.2021,
 abstract = {Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. The biases make the learned models unfair and can even exacerbate the marginalization of people. Considering that current debiasing methods for general natural language understanding tasks cannot effectively mitigate the biases in the toxicity detectors, we propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns (e.g., identity mentions, dialect) to toxicity labels. We empirically show that our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.},
 author = {Chuang, Yung-Sung and Gao, Mingye and Luo, Hongyin and Glass, James and Lee, Hung-yi and Chen, Yun-Nung and Li, Shang-Wen},
 date = {14.06.2021},
 title = {{Mitigating Biases in Toxic Language Detection through Invariant  Rationalization}},
 url = {http://arxiv.org/pdf/2106.07240v1},
 keywords = {Computer Science - Computation and Language},
 file = {Mitigating Biases in Toxic Language Detection through Invariant Rationalization:Attachments/Mitigating Biases in Toxic Language Detection through Invariant Rationalization.pdf:application/pdf}
}


@article{Cohen.2023,
 abstract = {Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach utilizing DeBERTa models, integrating back-translation and GPT-3 augmentation techniques during both training and test time. This method aims to address the complexities associated with detecting hate speech, resulting in more robust and accurate results. Our findings indicate that the proposed approach significantly enhances hate speech detection performance across various metrics and models in both the Parler and GAB datasets. For reproducibility and further exploration, our code is publicly available at https://github.com/OrKatz7/parler-hate-speech.},
 author = {Cohen, Seffi and Presil, Dan and Katz, Or and Arbili, Ofir and Messica, Shvat and Rokach, Lior},
 year = {2023},
 title = {{Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time}},
 pages = {101887},
 volume = {99},
 issn = {15662535},
 journal = {{Information Fusion}},
 doi = {10.1016/j.inffus.2023.101887}
}


@proceedings{Cohn.,
 title = {{Findings of the Association for Computational Linguistics: EMNLP 2020}},
 address = {Stroudsburg, PA, USA},
 publisher = {{Association for Computational Linguistics}},
 editor = {Cohn, Trevor and He, Yulan and Liu, Yang},
 doi = {10.18653/v1/2020.findings-emnlp}
}


@article{Collins.2018,
 author = {Collins, Erin},
 year = {2018},
 title = {{Punishing risk}},
 url = {https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/glj107§ion=6},
 pages = {57--108},
 number = {107},
 journal = {{Georgetown Law Journal}}
}


@book{Comim.2020,
 author = {Comim, Flavio and Borsi, Mih{\'a}ly Tam{\'a}s and {Valerio Mendoza}, Octasiano},
 year = {2020},
 title = {{The Multi-dimensions of Aporophobia}},
 urldate = {16.06.2023},
 publisher = {{IQS School of Management}}
}


@article{Crockett.2017,
 abstract = {Moral outrage is an ancient emotion that is now widespread on digital media and online social networks. How might these new technologies change the expression of moral outrage and its social consequences?},
 author = {Crockett, M. J.},
 year = {2017},
 title = {{Moral outrage in the digital age}},
 url = {https://www.nature.com/articles/s41562-017-0213-3},
 keywords = {Human behaviour;Humans;Internet;Morals;Psychology;Social Control, Informal/methods;Social Norms;Society},
 pages = {769--771},
 volume = {1},
 number = {11},
 issn = {2397-3374},
 journal = {{Nature Human Behaviour}},
 doi = {10.1038/s41562-017-0213-3}
}


@misc{Davani.2020,
 abstract = {Approaches for mitigating bias in supervised models are designed to reduce models' dependence on specific sensitive features of the input data, e.g., mentioned social groups. However, in the case of hate speech detection, it is not always desirable to equalize the effects of social groups because of their essential role in distinguishing outgroup-derogatory hate, such that particular types of hateful rhetoric carry the intended meaning only when contextualized around certain social group tokens. Counterfactual token fairness for a mentioned social group evaluates the model's predictions as to whether they are the same for (a) the actual sentence and (b) a counterfactual instance, which is generated by changing the mentioned social group in the sentence. Our approach assures robust model predictions for counterfactuals that imply similar meaning as the actual sentence. To quantify the similarity of a sentence and its counterfactual, we compare their likelihood score calculated by generative language models. By equalizing model behaviors on each sentence and its counterfactuals, we mitigate bias in the proposed model while preserving the overall classification performance.},
 author = {Davani, Aida Mostafazadeh and Omrani, Ali and Kennedy, Brendan and Atari, Mohammad and Ren, Xiang and Dehghani, Morteza},
 date = {2020},
year = {2020},
 title = {{Fair Hate Speech Detection through Evaluation of Social Group  Counterfactuals}},
 url = {http://arxiv.org/pdf/2010.12779v1},
 keywords = {Computer Science - Computation and Language},
 file = {Davani, Omrani et al 24102020 - Fair Hate Speech Detection:Attachments/Davani, Omrani et al 24102020 - Fair Hate Speech Detection.pdf:application/pdf}
}


@misc{Davidson.29.05.2019,
 abstract = {Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect.},
 author = {Davidson, Thomas and Bhattacharya, Debasmita and Weber, Ingmar},
 date = {29.05.2019},
 title = {{Racial Bias in Hate Speech and Abusive Language Detection Datasets}},
 url = {http://arxiv.org/pdf/1905.12516v1},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning},
 file = {Racial{\_}Bias{\_}in{\_}Hate{\_}Speech{\_}and{\_}Abusive{\_}Language{\_}Detection{\_}Datasets-1:Attachments/Racial{\_}Bias{\_}in{\_}Hate{\_}Speech{\_}and{\_}Abusive{\_}Language{\_}Detection{\_}Datasets-1.pdf:application/pdf}
}


@article{Dawkins.2021,
 abstract = {Reporting and providing test sets for harmful bias in NLP applications is essential for building a robust understanding of the current problem. We present a new observation of gender bias in a downstream NLP application: marked attribute bias in natural language inference. Bias in downstream applications can stem from training data, word embeddings, or be amplified by the model in use. However, focusing on biased word embeddings is potentially the most impactful first step due to their universal nature. Here we seek to understand how the intrinsic properties of word embeddings contribute to this observed marked attribute effect, and whether current post-processing methods address the bias successfully. An investigation of the current debiasing landscape reveals two open problems: none of the current debiased embeddings mitigate the marked attribute error, and none of the intrinsic bias measures are predictive of the marked attribute effect. By noticing that a new type of intrinsic bias measure correlates meaningfully with the marked attribute effect, we propose a new postprocessing debiasing scheme for static word embeddings. The proposed method applied to existing embeddings achieves new best results on the marked attribute bias test set. See https://github.com/hillary-dawkins/MAB.},
 author = {Dawkins, Hillary},
 year = {2021},
 title = {{Marked Attribute Bias in Natural Language Inference}},
 url = {http://arxiv.org/pdf/2109.14039v1},
 keywords = {Computer Science - Computation and Language},
 pages = {4214--4226},
 doi = {10.18653/v1/2021.findings-acl.369}
}


@inproceedings{Defrance.2023,
 abstract = {Fairness in AI has garnered quite some attention in research, and increasingly also in society. The so-called {\textquotedbl}Impossibility Theorem{\textquotedbl} has been one of the more striking research results with both theoretical and practical consequences, as it states that satisfying a certain combination of fairness measures is impossible. To date, this negative result has not yet been complemented with a positive one: a characterization of which combinations of fairness notions are possible. This work aims to fill this gap by identifying maximal sets of commonly used fairness measures that can be simultaneously satisfied. The fairness measures used are demographic parity, equal opportunity, predictive equality, predictive parity, false omission rate parity, overall accuracy equality and treatment equality. We conclude that in total 12 maximal sets of these fairness measures are possible, among which are seven combinations of two measures, and five combinations of three measures. Our work raises interesting questions regarding the practical relevance of each of these 12 maximal fairness notions in various scenarios.},
 author = {Defrance, Marybeth and de Bie, Tijl},
 title = {{Maximal fairness}},
 pages = {851--880},
 publisher = {ACM},
 isbn = {9798400701924},
 booktitle = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3593013.3594048}
}


@book{Devlin.2019,
 abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.  BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5{\%} (7.7{\%} point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
 author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
 year = {2019},
 title = {{BERT: Pre-training of Deep Bidirectional Transformers for Language  Understanding}},
 keywords = {Computer Science - Computation and Language},
 file = {Devlin, Chang et al. 2019 - BERT Pre-training of Deep Bidirectional:Attachments/Devlin, Chang et al. 2019 - BERT Pre-training of Deep Bidirectional.pdf:application/pdf}
}


@inproceedings{DiCiccio.2023,
 abstract = {Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected outcomes regardless of group. Testing and satisfying PP is especially important in many settings where model scores are interpreted by humans or directly provide access to opportunity, such as healthcare or banking. Solutions for PP violations have primarily been studied through the lens of model calibration. However, we find that existing calibration-based tests and mitigation methods are designed for independent data, which is often not assumable in large-scale applications such as social media or medical testing. In this work, we address this issue by developing a statistically rigorous non-parametric regression based test for PP with dependent observations. We then apply our test to illustrate that PP testing can significantly vary under the two assumptions. Lastly, we provide a mitigation solution to provide a minimally-biased post-processing transformation function to achieve PP.},
 author = {DiCiccio, Cyrus and Hsu, Brian and Yu, Yinyin and Nandy, Preetam and Basu, Kinjal},
 title = {{Detection and Mitigation of Algorithmic Bias via Predictive Parity}},
 pages = {1801--1816},
 publisher = {ACM},
 isbn = {9798400701924},
 booktitle = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3593013.3594117},
 file = {Detection and Mitigation of Algorithmic Bias via Predict:Attachments/Detection and Mitigation of Algorithmic Bias via Predict.pdf:application/pdf}
}


@inproceedings{Dixon.2018,
 abstract = {We introduce and illustrate a new approach to measuring and mitigating unintended bias in machine learning models. Our definition of unintended bias is parameterized by a test set and a subset of input features. We illustrate how this can be used to evaluate text classifiers using a synthetic test set and a public corpus of comments annotated for toxicity from Wikipedia Talk pages. We also demonstrate how imbalances in training data can lead to unintended bias in the resulting models, and therefore potentially unfair applications. We use a set of common demographic identity terms as the subset of input features on which we measure bias. This technique permits analysis in the common scenario where demographic information on authors and readers is unavailable, so that bias mitigation must focus on the content of the text itself. The mitigation method we introduce is an unsupervised approach based on balancing the training dataset. We demonstrate that this approach reduces the unintended bias without compromising overall model quality.},
 author = {Dixon, Lucas and Li, John and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
 title = {{Measuring and Mitigating Unintended Bias in Text Classification}},
 pages = {67--73},
 publisher = {ACM},
 isbn = {9781450360128},
 editor = {Furman, Jason and Marchant, Gary and Price, Huw and Rossi, Francesca},
 booktitle = {{Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society}},
 year = {2018},
 address = {New York, NY, USA},
 doi = {10.1145/3278721.3278729},
 file = {measuring and mitigating bias:Attachments/measuring and mitigating bias.pdf:application/pdf}
}


@article{Duntsch.2019,
 author = {D{\"u}ntsch, Ivo and Gediga, G{\"u}nther},
 year = {2019},
 title = {{Confusion Matrices and Rough Set Data Analysis}},
 url = {https://iopscience.iop.org/article/10.1088/1742-6596/1229/1/012055/meta},
 pages = {012055},
 volume = {1229},
 number = {1},
 issn = {1742-6596},
 journal = {{Journal of Physics: Conference Series}},
 doi = {10.1088/1742-6596/1229/1/012055},
 file = {D{\"u}ntsch, Gediga 2019 - Confusion Matrices and Rough Set:Attachments/D{\"u}ntsch, Gediga 2019 - Confusion Matrices and Rough Set.pdf:application/pdf}
}


@article{Dwork.2018,
 abstract = {Decoupled Classifiers for Group-Fair and Efficient Machine LearningCynthia Dwork,~Nicole Immorlica,~Adam Tauman Kalai,~Max LeisersonWhen it is...},
 author = {{Cynthia Dwork} and {Nicole Immorlica} and {Adam Tauman Kalai} and {Max Leiserson}},
 year = {2018},
 title = {{Decoupled Classifiers for Group-Fair and Efficient Machine Learning}},
 url = {https://proceedings.mlr.press/v81/dwork18a.html},
 pages = {119--133},
 issn = {2640-3498},
 journal = {{Conference on Fairness, Accountability and Transparency}},
 file = {Cynthia Dwork, Nicole Immorlica et al. 2018 - Decoupled Classifiers for Group-Fair:Attachments/Cynthia Dwork, Nicole Immorlica et al. 2018 - Decoupled Classifiers for Group-Fair.pdf:application/pdf}
}


@article{Elrahman.2014,
 author = {Elrahman, Shaza and Abraham, Ajith},
 year = {2014},
 title = {{A review of class imbalance problem}},
 url = {http://ias04.softcomputing.net/jnic2.pdf},
 pages = {332--340},
 volume = {1},
 journal = {{Journal of Network and Innovative Computing}},
 file = {A review of class imbalance:Attachments/A review of class imbalance.pdf:application/pdf}
}


@book{Evans.2023,
 abstract = {Human biases have been shown to influence the performance of models and algorithms

in various fields, including Natural Language Processing. While the study of this phe-

nomenon is garnering focus in recent years, the available resources are still relatively

scarce, often focusing on different forms or manifestations of biases. The aim of our work

is twofold: 1) gather publicly-available datasets and determine how to better combine them

to effectively train models in the task of hate speech detection and classification; 2) anal-

yse the main issues with these datasets, such as scarcity, skewed resources, and reliance on

non-persistent data. We discuss these issues in tandem with the development of our exper-

iments, in which we show that the combinations of different datasets greatly impact the

models' performance.},
 author = {Evans, Ana Sofia and Helena, Moniz and Luisa, Coheur},
 year = {2023},
 title = {{A Study on Bias Detection and Classification in Natural Language Processing}},
 doi = {10.21203/rs.3.rs-3351695/v1}
}


@misc{Fanton.2021,
 abstract = {Undermining the impact of hateful content with informed and non-aggressive responses, called counter narratives, has emerged as a possible solution for having healthier online communities. Thus, some NLP studies have started addressing the task of counter narrative generation. Although such studies have made an effort to build hate speech / counter narrative (HS/CN) datasets for neural generation, they fall short in reaching either high-quality and/or high-quantity. In this paper, we propose a novel human-in-the-loop data collection methodology in which a generative language model is refined iteratively by using its own data from the previous loops to generate new training samples that experts review and/or post-edit. Our experiments comprised several loops including dynamic variations. Results show that the methodology is scalable and facilitates diverse, novel, and cost-effective data collection. To our knowledge, the resulting dataset is the only expert-based multi-target HS/CN dataset available to the community.},
 author = {Fanton, Margherita and Bonaldi, Helena and Tekiro{\u{g}}lu, Serra Sinem and Guerini, Marco},
 year = {2021},
 title = {{Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech}},
 url = {http://arxiv.org/pdf/2107.08720.pdf},
 keywords = {Computation and Language (cs.CL);Computers and Society (cs.CY)},
 doi = {10.18653/v1/2021.acl-long.250},
 file = {Fanton, Bonaldi et al. 2021 - Human-in-the-Loop for Data Collection:Attachments/Fanton, Bonaldi et al. 2021 - Human-in-the-Loop for Data Collection.pdf:application/pdf}
}


@misc{Feldman.2021,
 abstract = {Machine Learning models have been deployed across many different aspects of society, often in situations that affect social welfare. Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly based on protected attributes such as gender. In this paper, we introduce several examples of machine learning gender bias in practice followed by formalizations of fairness. We provide a survey of fairness research by detailing influential pre-processing, in-processing, and post-processing bias mitigation algorithms. We then propose an end-to-end bias mitigation framework, which employs a fusion of pre-, in-, and post-processing methods to leverage the strengths of each individual technique. We test this method, along with the standard techniques we review, on a deep neural network to analyze bias mitigation in a deep learning setting. We find that our end-to-end bias mitigation framework outperforms the baselines with respect to several fairness metrics, suggesting its promise as a method for improving fairness. As society increasingly relies on artificial intelligence to help in decision-making, addressing gender biases present in deep learning models is imperative. To provide readers with the tools to assess the fairness of machine learning models and mitigate the biases present in them, we discuss multiple open source packages for fairness in AI.

9 pages, 1 figure},
 author = {Feldman, Tal and Peake, Ashley},
 date = {2021},
 year = {2021},
 title = {{End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning}},
 keywords = {Artificial Intelligence (cs.AI);Computers and Society (cs.CY);FOS: Computer and information sciences;Machine Learning (cs.LG)},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2104.02532},
 file = {end to end bias mitigation:Attachments/end to end bias mitigation.pdf:application/pdf}
}


@book{Feng.2021,
 abstract = {Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area. We also present a GitHub repository with a paper list that will be continuously updated at https://github.com/styfeng/DataAug4NLP},
 author = {Feng, Steven Y. and Gangal, Varun and Wei, Jason and Chandar, Sarath and Vosoughi, Soroush and Mitamura, Teruko and Hovy, Eduard},
 year = {2021},
 title = {{A Survey of Data Augmentation Approaches for NLP}},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computation and Language;Computer Science - Learning},
 file = {Feng, Gangal et al. 2021 - A Survey of Data Augmentation:Attachments/Feng, Gangal et al. 2021 - A Survey of Data Augmentation.pdf:application/pdf}
}


@inproceedings{Finocchiaro.2021,
 author = {Finocchiaro, Jessie and Maio, Roland and Monachou, Faidra and Patro, Gourab K. and Raghavan, Manish and Stoica, Ana-Andreea and Tsirtsis, Stratis},
 title = {{Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness}},
 publisher = {ACM},
 booktitle = {{Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2021},
 address = {New York, NY, USA},
 doi = {10.1145/3442188.3445912},
 file = {Finocchiaro, Maio et al. 2021 - Bridging Machine Learning and Mechanism:Attachments/Finocchiaro, Maio et al. 2021 - Bridging Machine Learning and Mechanism.pdf:application/pdf}
}


@proceedings{Fisch.2019,
 year = {2019},
 title = {{Proceedings of the 2nd Workshop on Machine Reading for Question Answering}},
 address = {Stroudsburg, PA, USA},
 publisher = {{Association for Computational Linguistics}},
 editor = {Fisch, Adam and Talmor, Alon and Jia, Robin and Seo, Minjoon and Choi, Eunsol and Chen, Danqi},
 doi = {10.18653/v1/D19-58}
}


@article{Founta.2018,
 abstract = {In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels. By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration.},
 author = {Founta, Antigoni and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas},
 year = {2018},
 title = {{Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior}},
 url = {https://ojs.aaai.org/index.php/icwsm/article/view/14991},
 keywords = {abusive;crowdsourcing;hate speech;twitter},
 volume = {12},
 number = {1},
 issn = {2334-0770},
 journal = {{Proceedings of the International AAAI Conference on Web and Social Media}},
 doi = {10.1609/icwsm.v12i1.14991},
 file = {Founta, Djouvas et al. 2018 - Large Scale Crowdsourcing and Characterization:Attachments/Founta, Djouvas et al. 2018 - Large Scale Crowdsourcing and Characterization.pdf:application/pdf}
}


@inproceedings{Friedler.2019,
 author = {Friedler, Sorelle A. and Scheidegger, Carlos and Venkatasubramanian, Suresh and Choudhary, Sonam and Hamilton, Evan P. and Roth, Derek},
 title = {{A comparative study of fairness-enhancing interventions in machine learning}},
 publisher = {ACM},
 booktitle = {{Proceedings of the Conference on Fairness, Accountability, and Transparency}},
 year = {2019},
 address = {New York, NY, USA},
 doi = {10.1145/3287560.3287589},
 file = {Friedler, Scheidegger et al. 2019 - A comparative study of fairness-enhancing:Attachments/Friedler, Scheidegger et al. 2019 - A comparative study of fairness-enhancing.pdf:application/pdf}
}


@book{Frohling.2023,
 abstract = {This paper proposes a template for documenting datasets that have been collected from online platforms for research purposes. The template should help to critically reflect on data quality and increase transparency in research fields that make use of online platform data. The paper describes our motivation, outlines the procedure for developing a specific documentation template that we refer to as TES-D (Total Error Sheets for Datasets) and has the current version of the template, guiding questions and a manual attached as supplementary material. The TES-D approach builds upon prior work in designing error frameworks for data from online platforms, namely the Total Error Framework for digital traces of human behavior on online platforms (TED-On, https://doi.org/10.1093/poq/nfab018).}   ,
 author = {Fr{\"o}hling, Leon and Sen, Indira and Soldner, Felix and Steinbrinker, Leonie and Zens, Maria and Weller, Katrin},
 year = {2023},
 title = {{Total Error Sheets for Datasets (TES-D) -- A Critical Guide to  Documenting Online Platform Datasets}},
 keywords = {Computer Science - Computers and Society;Computer Science - Databases},
 file = {Fr{\"o}hling, Sen et al. 2023 - Total Error Sheets for Datasets:Attachments/Fr{\"o}hling, Sen et al. 2023 - Total Error Sheets for Datasets.pdf:application/pdf}
}


@proceedings{Furman.2018,
 year = {2018},
 title = {{Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9781450360128},
 editor = {Furman, Jason and Marchant, Gary and Price, Huw and Rossi, Francesca},
 doi = {10.1145/3278721}
}


@inproceedings{Gaci.2022,
 abstract = {Natural Language Processing (NLP) models are found to exhibit discriminatory stereotypes across many social constructs, e.g. gender and race. In comparison to the progress made in reducing bias from static word embeddings, fairness in sentence-level text encoders received little consideration despite their wider applicability in contemporary NLP tasks. In this paper, we propose a debiasing method for pre-trained text encoders that both reduces social stereotypes, and inflicts next to no semantic damage. Unlike previous studies that directly manipulate the embeddings, we suggest to dive deeper into the operation of these encoders, and pay more attention to the way they pay attention to different social groups. We find that stereotypes are also encoded in the attention layer. Then, we work on model debiasing by redistributing the attention scores of a text encoder such that it forgets any preference to historically advantaged groups, and attends to all social classes with the same intensity. Our experiments confirm that reducing bias from attention effectively mitigates it from the model's text representations.},
 author = {Gaci, Yacine and Benatallah, Boualem and Casati, Fabio and Benabdeslem, Khalid},
 title = {{Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention}},
 pages = {9582--9602},
 publisher = {{Association for Computational Linguistics}},
 editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
 booktitle = {{Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}},
 year = {2022},
 address = {Stroudsburg, PA, USA},
 doi = {10.18653/v1/2022.emnlp-main.651},
 file = {Debiasing{\_}Pretrained{\_}Text{\_}Encoders{\_}by{\_}Paying{\_}Attention{\_}to{\_}Paying{\_}Attention:Attachments/Debiasing{\_}Pretrained{\_}Text{\_}Encoders{\_}by{\_}Paying{\_}Attention{\_}to{\_}Paying{\_}Attention.pdf:application/pdf}
}


@inproceedings{Garcia.2009,
 abstract = {This paper introduces a new metric, named Index of Balanced Accuracy, for evaluating learning processes in two-class imbalanced domains. The method combines an unbiased index of its overall accuracy and a measure about how dominant is the class with the highest...},
 author = {Garc{\'i}a, V. and Mollineda, R. A. and S{\'a}nchez, J. S.},
 title = {{Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions}},
 url = {https://link.springer.com/chapter/10.1007/978-3-642-02172-5_57} ,
 pages = {441--448},
 publisher = {Springer},
 isbn = {978-3-642-02172-5},
 series = {{Lecture Notes in Computer Science}},
 editor = {Araujo, Helder},
 booktitle = {{Pattern recognition and image analysis}},
 year = {2009},
 address = {Berlin and Heidelberg},
 doi = {10.1007/978-3-642-02172-5{\textunderscore }57}
}


@inproceedings{Gardner.2019,
 abstract = {D19-58 2019},
 author = {Gardner, Matt and Berant, Jonathan and Hajishirzi, Hannaneh and Talmor, Alon and Min, Sewon},
 title = {{On Making Reading Comprehension More Comprehensive}},
 pages = {105--112},
 publisher = {{Association for Computational Linguistics}},
 editor = {Fisch, Adam and Talmor, Alon and Jia, Robin and Seo, Minjoon and Choi, Eunsol and Chen, Danqi},
 booktitle = {{Proceedings of the 2nd Workshop on Machine Reading for Question Answering}},
 year = {2019},
 address = {Stroudsburg, PA, USA},
 doi = {10.18653/v1/D19-5815},
 file = {On making reading comprehension more comprehensive:Attachments/On making reading comprehension more comprehensive.pdf:application/pdf}
}


@misc{Garg.2022,
 abstract = {Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors such as the context, geography, socio-political climate, and background of the producers and consumers of the posts play a crucial role in determining if the content can be flagged as toxic. Adoption of automated toxicity detection models in production can thus lead to a sidelining of the various groups they aim to help in the first place. It has piqued researchers' interest in examining unintended biases and their mitigation. Due to the nascent and multi-faceted nature of the work, complete literature is chaotic in its terminologies, techniques, and findings. In this paper, we put together a systematic study of the limitations and challenges of existing methods for mitigating bias in toxicity detection.  We look closely at proposed methods for evaluating and mitigating bias in toxic speech detection. To examine the limitations of existing methods, we also conduct a case study to introduce the concept of bias shift due to knowledge-based bias mitigation. The survey concludes with an overview of the critical challenges, research gaps, and future directions. While reducing toxicity on online platforms continues to be an active area of research, a systematic study of various biases and their mitigation strategies will help the research community produce robust and fair models.},
 author = {Garg, Tanmay and Masud, Sarah and Suresh, Tharun and Chakraborty, Tanmoy},
 date = {2022},
 year = {2022},
 title = {{Handling Bias in Toxic Speech Detection: A Survey}},
 url = {http://arxiv.org/pdf/2202.00126v3},
 keywords = {bias mitigation;Computer Science - Computers and Society;Computer Science - Learning;hate speech;social networks;toxic speech;unintended bias},
 file = {Handling Bias in Toxic Speech Detection A Survey:Attachments/Handling Bias in Toxic Speech Detection A Survey.pdf:application/pdf;Garg, Masud et al 26012022 - Handling Bias in Toxic Speech:Attachments/Garg, Masud et al 26012022 - Handling Bias in Toxic Speech.pdf:application/pdf}
}


@phdthesis{Geleta.2023,
 abstract = {eng: Hate speech has intensified to pose a serious issue on social media. This study explores methods for effectively reducing its negative consequences by assisting content moder- ation with machine learning (ML). Hate speech is categorized into four intensities: no hate, intimidation, offense or discrimination, and promotes violence, in order to give a more true spectrum of intensity and get over the limitations of viewing hate speech as a binary task (as is typical in sentiment analysis). For this, a dataset in English and German was first annotated by 31 users. In order to enhance interpretability and transparency, the ML system has been incorporated into a dashboard that provides explainable artificial intelligence (XAI) features. The effectiveness of the proposed XAI dashboard in assisting content moderation is assessed by conducting a case study with 40 non-expert moder- ators. According to the findings, determining the severity of hate speech is crucial for content moderators since it can influence the severity of potential punishments. Similar to this, XAI appears to be a potential technique to increase ML trustworthiness, helping the informed decision-making of moderators. Hassreden haben sich zu einem ernsten Problem in den sozialen Medien entwickelt. In dieser Arbeit werden Methoden zur wirksamen Bek{\"a}mpfung dieser negativen Folgen untersucht, indem die Moderation von Inhalten durch maschinelles Lernen (ML) un- terst{\"u}tzt wird. Hassrede wird in vier Intensit{\"a}ten eingeteilt: kein Hass, Einsch{\"u}chterung, Beleidigung oder Diskriminierung und Anstiftung zur Gewalt, um ein realistischeres Spektrum der Intensit{\"a}t zu bieten und die Einschr{\"a}nkungen zu {\"u}berwinden, Hassrede als bin{\"a}re Aufgabe zu betrachten (wie es typischerweise bei der Sentimentanalyse der Fall ist). Daf{\"u}r wurde zun{\"a}chst ein Datensatz in Englisch und Deutsch von 31 Benutzern annotiert. Um die Interpretierbarkeit und Transparenz zu verbessern, wurde das ML in ein Dashboard integriert, welches erkl{\"a}rbare k{\"u}nstliche Intelligenz (XAI) Methoden zur Erkl{\"a}rung von ML-Modellen verwendet. Die Wirksamkeit des vorgeschlagenen XAI-Dashboards bei der Unterst{\"u}tzung der Inhaltsmoderation wird in einer Fallstudie mit 40 Moderatoren bewer- tet, die keine Experten auf dem Gebiet sind. Laut den Ergebnissen ist die Bestimmung der Schwere von Hassrede f{\"u}r Inhaltsmoderatoren von entscheidender Bedeutung, da sie die Schwere m{\"o}glicher Strafen beeinflussen kann. {\"A}hnlich wie bei diesem scheint XAI eine potenzielle Technik zur Erh{\"o}hung der Vertrauensw{\"u}rdigkeit von ML zu sein und hilft bei der informierten Entscheidungsfindung von Moderatoren.},
 author = {Geleta, Raisa Romanov},
 year = {2023},
 title = {{Exploring the Role of AI and XAI in Hate Speech Detection on Social Media: A Study on User Trust}},
 url = {https://resolver.obvsg.at/urn:nbn:at:at-ubl:1-63964},
 keywords = {Hochschulschrift},
 address = {Austria},
 school = {{Johannes Kepler University Linz}},
 type = {{Master Thesis}},
 file = {Exploring the Role of AI and XAI in Hate Speech Detection on Social Media A Study on User Trust:Attachments/Exploring the Role of AI and XAI in Hate Speech Detection on Social Media A Study on User Trust.pdf:application/pdf}
}


@inproceedings{Ghosh.2023,
 abstract = {Fairness in machine learning has attained significant focus due to the widespread application in high-stake decision-making tasks. Unregulated machine learning classifiers can exhibit bias towards certain demographic groups in data, thus the quantification and mitigation of classifier bias is a central concern in fairness in machine learning. In this paper, we aim to quantify the influence of different features in a dataset on the bias of a classifier. To do this, we introduce the Fairness Influence Function (FIF). This function breaks down bias into its components among individual features and the intersection of multiple features. The key idea is to represent existing group fairness metrics as the difference of the scaled conditional variances in the classifier's prediction and apply a decomposition of variance according to global sensitivity analysis. To estimate FIFs, we instantiate an algorithm that applies variance decomposition of classifier's prediction following local regression. Experiments demonstrate that captures FIFs of individual feature and intersectional features, provides a better approximation of bias based on FIFs, demonstrates higher correlation of FIFs with fairness interventions, and detects changes in bias due to fairness affirmative/punitive actions in the classifier.



The code is available at https://github.com/ReAILe/bias-explainer. The extended version of the paper is at https://arxiv.org/pdf/2206.00667.pdf.},
 author = {Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.},
 title = {{``How Biased are Your Features?'': Computing Fairness Influence Functions with Global Sensitivity Analysis}},
 pages = {138--148},
 publisher = {ACM},
 isbn = {9798400701924},
 booktitle = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3593013.3593983}
}


@misc{Giorgi.04.02.2023,
 abstract = {Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people who use substances have not. This paper explores stigma toward PWUS using a data set of roughly 5,000 public Reddit posts. We performed a crowd-sourced annotation task where workers are asked to annotate each post for the presence of stigma toward PWUS and answer a series of questions related to their experiences with substance use. Results show that workers who use substances or know someone with a substance use disorder are more likely to rate a post as stigmatizing. Building on this, we use a supervised machine learning framework that centers workers with lived substance use experience to label each Reddit post as stigmatizing. Modeling person-level demographics in addition to comment-level language results in a classification accuracy (as measured by AUC) of 0.69 -- a 17{\%} increase over modeling language alone. Finally, we explore the linguist cues which distinguish stigmatizing content: PWUS substances and those who don't agree that language around othering ({\textquotedbl}people{\textquotedbl}, {\textquotedbl}they{\textquotedbl}) and terms like {\textquotedbl}addict{\textquotedbl} are stigmatizing, while PWUS (as opposed to those who do not) find discussions around specific substances more stigmatizing. Our findings offer insights into the nature of perceived stigma in substance use. Additionally, these results further establish the subjective nature of such machine learning tasks, highlighting the need for understanding their social contexts.},
 author = {Giorgi, Salvatore and Bellew, Douglas and Habib, Daniel Roy Sadek and Sherman, Garrick and Sedoc, Joao and Smitterberg, Chase and Devoto, Amanda and Himelein-Wachowiak, McKenzie and Curtis, Brenda},
 date = {04.02.2023},
 title = {{Lived Experience Matters: Automatic Detection of Stigma on Social Media  Toward People Who Use Substances}},
 url = {http://arxiv.org/pdf/2302.02064v2},
 keywords = {Computer Science - Computation and Language}
}


@proceedings{Goldberg.2022,
 year = {2022},
 title = {{Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}},
 address = {Stroudsburg, PA, USA},
 publisher = {{Association for Computational Linguistics}},
 editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
 doi = {10.18653/v1/2022.emnlp-main}
}


@inproceedings{Gordon.2021,
 author = {Gordon, Mitchell L. and Zhou, Kaitlyn and Patel, Kayur and Hashimoto, Tatsunori and Bernstein, Michael S.},
 title = {{The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality}},
 publisher = {ACM},
 booktitle = {{Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}},
 year = {2021},
 address = {New York, NY, USA},
 doi = {10.1145/3411764.3445423}
}


@misc{Goswami.15.06.2023,
 abstract = {Machine learning (ML) models are only as good as the data they are trained on. But recent studies have found datasets widely used to train and evaluate ML models, e.g. ImageNet, to have pervasive labeling errors. Erroneous labels on the train set hurt ML models' ability to generalize, and they impact evaluation and model selection using the test set. Consequently, learning in the presence of labeling errors is an active area of research, yet this field lacks a comprehensive benchmark to evaluate these methods. Most of these methods are evaluated on a few computer vision datasets with significant variance in the experimental protocols. With such a large pool of methods and inconsistent evaluation, it is also unclear how ML practitioners can choose the right models to assess label quality in their data. To this end, we propose a benchmarking environment AQuA to rigorously evaluate methods that enable machine learning in the presence of label noise. We also introduce a design space to delineate concrete design choices of label error detection models. We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning tools facing mislabeled data.},
 author = {Goswami, Mononito and Sanil, Vedant and Choudhry, Arjun and Srinivasan, Arvind and Udompanyawit, Chalisa and Dubrawski, Artur},
 date = {15.06.2023},
 title = {{AQuA: A Benchmarking Tool for Label Quality Assessment}},
 url = {http://arxiv.org/pdf/2306.09467v1},
 keywords = {Computer Science - Learning}
}


@misc{Gupta.14.06.2023,
 abstract = {Deep neural networks often learn unintended biases during training, which might have harmful effects when deployed in real-world settings. This paper surveys 209 papers on bias in NLP models, most of which address sociodemographic bias. To better understand the distinction between bias and real-world harm, we turn to ideas from psychology and behavioral economics to propose a definition for sociodemographic bias. We identify three main categories of NLP bias research: types of bias, quantifying bias, and debiasing. We conclude that current approaches on quantifying bias face reliability issues, that many of the bias metrics do not relate to real-world biases, and that current debiasing techniques are superficial and hide bias rather than removing it. Finally, we provide recommendations for future work.},
 author = {Gupta, Vipul and Venkit, Pranav Narayanan and Wilson, Shomir and Passonneau, Rebecca J.},
 date = {14.06.2023},
 title = {{Survey on Sociodemographic Bias in Natural Language Processing}},
 url = {http://arxiv.org/pdf/2306.08158v1},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computation and Language;Computer Science - Learning}
}


@article{Gupta.2021,
 abstract = {Fear of missing out (FoMO) is a unique term introduced in 2004 to describe a phenomenon observed on social networking sites. FoMO includes two processes; firstly, perception of missing out, followed up with a compulsive behavior to maintain these social connections. We are interested in understanding the complex construct of FoMO and its relations to the need to belong and form stable interpersonal relationships. It is associated with a range of negative life experiences and feelings, due to it being considered a problematic attachment to social media. We have provided a general review of the literature and have summarized the findings in relation to mental health, social functioning, sleep, academic performance and productivity, neuro-developmental disorders, and physical well-being. We have also discussed the treatment options available for FoMo based on cognitive behavior therapy. It imperative that new findings on FoMO are communicated to the clinical community as it has diagnostic implications and could be a confounding variable in those who do not respond to treatment as usual.},
 author = {Gupta, Mayank and Sharma, Aditya},
 year = {2021},
 title = {{Fear of missing out: A brief overview of origin, theoretical underpinnings and relationship with mental health}},
 url = {https://pubmed.ncbi.nlm.nih.gov/34307542/},
 pages = {4881--4889},
 volume = {9},
 number = {19},
 issn = {2307-8960},
 journal = {{World journal of clinical cases}},
 doi = {10.12998/wjcc.v9.i19.4881},
 file = {Gupta, Sharma 2021 - Fear of missing out:Attachments/Gupta, Sharma 2021 - Fear of missing out.pdf:application/pdf}
}


@misc{Han.2021,
 abstract = {Adversarial learning can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another. Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and the stability of training.},
 author = {Han, Xudong and Baldwin, Timothy and Cohn, Trevor},
 date = {2021},
year = {2021},
 title = {{Diverse Adversaries for Mitigating Bias in Training}},
 url = {http://arxiv.org/pdf/2101.10001.pdf},
 keywords = {Artificial Intelligence (cs.AI);Computer Science - Artificial Intelligence;Computer Science - Learning;Machine Learning (cs.LG)},
 file = {Han, Baldwin et al. 25.01.2021 - Diverse Adversaries for Mitigating Bias:Attachments/Han, Baldwin et al. 25.01.2021 - Diverse Adversaries for Mitigating Bias.pdf:application/pdf}
}


@misc{Hartvigsen.2022,
 abstract = {Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language. To help mitigate these issues, we create ToxiGen, a new large-scale and machine-generated dataset of 274k toxic and benign statements about 13 minority groups. We develop a demonstration-based prompting framework and an adversarial classifier-in-the-loop decoding method to generate subtly toxic and benign text with a massive pretrained language model. Controlling machine generation in this way allows ToxiGen to cover implicitly toxic text at a larger scale, and about more demographic groups, than previous resources of human-written text. We conduct a human evaluation on a challenging subset of ToxiGen and find that annotators struggle to distinguish machine-generated text from human-written language. We also find that 94.5{\%} of toxic examples are labeled as hate speech by human annotators. Using three publicly-available datasets, we show that finetuning a toxicity classifier on our data improves its performance on human-written data substantially. We also demonstrate that ToxiGen can be used to fight machine-generated toxicity as finetuning improves the classifier significantly on our evaluation subset. Our code and data can be found at https://github.com/microsoft/ToxiGen.},
 author = {Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece},
 year = {2022},
 title = {{ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and  Implicit Hate Speech Detection}},
 url = {http://arxiv.org/pdf/2203.09509.pdf},
 keywords = {Computation and Language (cs.CL);Computer Science - Computation and Language},
 file = {Hartvigsen, Gabriel et al. 17.03.2022 - ToxiGen A Large-Scale Machine-Generated Dataset:Attachments/Hartvigsen, Gabriel et al. 17.03.2022 - ToxiGen A Large-Scale Machine-Generated Dataset.pdf:application/pdf;Hartvigsen, Gabriel et al 2022 - ToxiGen A Large-Scale Machine-Generated Dataset:Attachments/Hartvigsen, Gabriel et al 2022 - ToxiGen A Large-Scale Machine-Generated Dataset.pdf:application/pdf}
}


@inproceedings{Hauzenberger.2023,
 abstract = {Societal biases are reflected in large pre-trained language models and their fine-tuned versions on downstream tasks. Common in-processing bias mitigation approaches, such as adversarial training and mutual information removal, introduce additional optimization criteria, and update the model to reach a new debiased state. However, in practice, end-users and practitioners might prefer to switch back to the original model, or apply debiasing only on a specific subset of protected attributes. To enable this, we propose a novel modular bias mitigation approach, consisting of stand-alone highly sparse debiasing subnetworks, where each debiasing module can be integrated into the core model on-demand at inference time. Our approach draws from the concept of diff pruning, and proposes a novel training regime adaptable to various representation disentanglement optimizations. We conduct experiments on three classification tasks with gender, race, and age as protected attributes. The results show that our modular approach, while maintaining task performance, improves (or at least remains on-par with) the effectiveness of bias mitigation in comparison with baseline finetuning. Particularly on a two-attribute dataset, our approach with separately learned debiasing subnetworks shows effective utilization of either or both the subnetworks for selective bias mitigation.},
 author = {Hauzenberger, Lukas and Masoudian, Shahed and Kumar, Deepak and Schedl, Markus and Rekabsaz, Navid},
 title = {{Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks}},
 url = {https://aclanthology.org/2023.findings-acl.386},
 pages = {6192--6214},
 publisher = {{Association for Computational Linguistics}},
 booktitle = {{Findings of the Association for Computational Linguistics: ACL 2023}},
 year = {2023},
 address = {Toronto, Canada},
 file = {modular on demand debiasing:Attachments/modular on demand debiasing.pdf:application/pdf}
}


@misc{Hebert.18.07.2023,
 abstract = {We present the Multi-Modal Discussion Transformer (mDT), a novel multi-modal graph-based transformer model for detecting hate speech in online social networks. In contrast to traditional text-only methods, our approach to labelling a comment as hate speech centers around the holistic analysis of text and images. This is done by leveraging graph transformers to capture the contextual relationships in the entire discussion that surrounds a comment, with interwoven fusion layers to combine text and image embeddings instead of processing different modalities separately. We compare the performance of our model to baselines that only process text; we also conduct extensive ablation studies. We conclude with future work for multimodal solutions to deliver social value in online contexts, arguing that capturing a holistic view of a conversation greatly advances the effort to detect anti-social behavior.},
 author = {Hebert, Liam and Sahu, Gaurav and Sreenivas, Nanda Kishore and Golab, Lukasz and Cohen, Robin},
 date = {18.07.2023},
 title = {{Multi-Modal Discussion Transformer: Integrating Text, Images and Graph  Transformers to Detect Hate Speech on Social Media}},
 url = {http://arxiv.org/pdf/2307.09312v1},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning;Computer Science - Multimedia}
}


@article{HeinrichJiang.2020,
 abstract = {Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematical formulation of how this bias can arise. We do so by assuming the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases against certain groups. Despite the fact that we only observe the biased labels, we are able to show that the bias may nevertheless be corrected by re-weighting the data points without changing the labels. We show, with theoretical guarantees, that training on the re-weighted dataset corresponds to training on the unobserved but unbiased labels, thus leading to an unbiased machine learning classifier. Our procedure is fast and robust and can be used with virtually any learning algorithm. We evaluate on a number of standard machine learning fairness datasets and a variety of fairness notions, finding that our method outperforms standard approaches in achieving fair classification.},
 author = {{Heinrich Jiang} and {Ofir Nachum}},
 year = {2020},
 title = {{Identifying and Correcting Label Bias in Machine Learning}},
 url = {https://proceedings.mlr.press/v108/jiang20a},
 pages = {702--712},
 issn = {2640-3498},
 journal = {{International Conference on Artificial Intelligence and Statistics}},
 file = {Heinrich Jiang, Ofir Nachum 2020 - Identifying and Correcting Label Bias:Attachments/Heinrich Jiang, Ofir Nachum 2020 - Identifying and Correcting Label Bias.pdf:application/pdf}
}


@article{HeinrichJiang.2020b,
 abstract = {Identifying and Correcting Label Bias in Machine LearningHeinrich Jiang,~Ofir NachumDatasets often contain biases which unfairly disadvantage certain gr...},
 author = {{Heinrich Jiang} and {Ofir Nachum}},
 year = {2020},
 title = {{Identifying and Correcting Label Bias in Machine Learning}},
 url = {https://proceedings.mlr.press/v108/jiang20a},
 pages = {702--712},
 issn = {2640-3498},
 journal = {{International Conference on Artificial Intelligence and Statistics}},
 file = {Heinrich Jiang, Ofir Nachum 2020 - Identifying and Correcting Label Bias (2):Attachments/Heinrich Jiang, Ofir Nachum 2020 - Identifying and Correcting Label Bias (2).pdf:application/pdf}
}


@inproceedings{Hort.2021,
 abstract = {The increasingly wide uptake of Machine Learning (ML) has raised the significance of the problem of tackling bias (i.e., unfairness), making it a primary software engineering concern. In this paper, we introduce Fairea, a model behaviour mutation approach to benchmarking ML bias mitigation methods. We also report on a large-scale empirical study to test the effectiveness of 12 widely-studied bias mitigation methods. Our results reveal that, surprisingly, bias mitigation methods have a poor effectiveness in 49{\%} of the cases. In particular, 15{\%} of the mitigation cases have worse fairness-accuracy trade-offs than the baseline established by Fairea; 34{\%} of the cases have a decrease in accuracy and an increase in bias.



Fairea has been made publicly available for software engineers and researchers to evaluate their bias mitigation methods.},
 author = {Hort, Max and Zhang, Jie M. and Sarro, Federica and Harman, Mark},
 title = {{Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods}},
 pages = {994--1006},
 publisher = {ACM},
 isbn = {9781450385626},
 editor = {Spinellis, Diomidis and Gousios, Georgios and Chechik, Marsha and {Di Penta}, Massimiliano},
 booktitle = {{Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering}},
 year = {2021},
 address = {New York, NY, USA},
 doi = {10.1145/3468264.3468565}
}


@misc{Hsu.2022,
 abstract = {The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneously satisfy all three common and intuitive definitions of fairness - demographic parity, equalized odds, and predictive rate parity. This result has driven most works to focus on solutions for one or two of the metrics. Rather than follow suit, in this paper we present a framework that pushes the limits of the impossibility theorem in order to satisfy all three metrics to the best extent possible. We develop an integer-programming based approach that can yield a certifiably optimal post-processing method for simultaneously satisfying multiple fairness criteria under small violations. We show experiments demonstrating that our post-processor can improve fairness across the different definitions simultaneously with minimal model performance reduction. We also discuss applications of our framework for model selection and fairness explainability, thereby attempting to answer the question: who's the fairest of them all?},
 author = {Hsu, Brian and Mazumder, Rahul and Nandy, Preetam and Basu, Kinjal},
 date = {2022},
 title = {{Pushing the limits of fairness impossibility: Who's the fairest of them all?}},
 url = {https://arxiv.org/pdf/2208.12606},
 keywords = {Applications (stat.AP);Artificial Intelligence (cs.AI);Computers and Society (cs.CY);Machine Learning (cs.LG)},
 file = {Pushing the limits of fairness impossibility Whos the fa:Attachments/Pushing the limits of fairness impossibility Whos the fa.pdf:application/pdf}
}


@article{Hui.2021,
 abstract = {Modern neural architectures for classification tasks are trained using the cross-entropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be well-founded. We explore several major neural architectures and a range of standard benchmark datasets for NLP, automatic speech recognition (ASR) and computer vision tasks to show that these architectures, with the same hyper-parameter settings as reported in the literature, perform comparably or better when trained with the square loss, even after equalizing computational resources. Indeed, we observe that the square loss produces better results in the dominant majority of NLP and ASR experiments. Cross-entropy appears to have a slight edge on computer vision tasks.  We argue that there is little compelling empirical or theoretical evidence indicating a clear-cut advantage to the cross-entropy loss. Indeed, in our experiments, performance on nearly all non-vision tasks can be improved, sometimes significantly, by switching to the square loss. Furthermore, training with square loss appears to be less sensitive to the randomness in initialization. We posit that training using the square loss for classification needs to be a part of best practices of modern deep learning on equal footing with cross-entropy.},
 author = {Hui, Like and Belkin, Mikhail},
 year = {2021},
 title = {{Evaluation of Neural Architectures Trained with Square Loss vs  Cross-Entropy in Classification Tasks}},
 keywords = {Computer Science - Learning;Statistics - Machine Learning},
 journal = {{ICLR}},
 file = {Hui, Belkin 2021 - Evaluation of Neural Architectures Trained:Attachments/Hui, Belkin 2021 - Evaluation of Neural Architectures Trained.pdf:application/pdf}
}


@article{Islam.2020,
 abstract = {Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.},
 author = {Islam, Md Rafiqul and Liu, Shaowu and Wang, Xianzhi and Xu, Guandong},
 year = {2020},
 title = {{Deep learning for misinformation detection on online social networks: a survey and new perspectives}},
 url = {https://link.springer.com/article/10.1007/s13278-020-00696-x},
 keywords = {Applications of Graph Theory and Complex Networks;Data Mining and Knowledge Discovery;Economics;Game Theory;Humanities;Law;Methodology of the Social Sciences;Social and Behav. Sciences;Statistics for Social Sciences},
 pages = {82},
 volume = {10},
 number = {1},
 issn = {1869-5469},
 journal = {{Social Network Analysis and Mining}},
 doi = {10.1007/s13278-020-00696-x},
 file = {Islam, Liu et al. 2020 - Deep learning for misinformation detection:Attachments/Islam, Liu et al. 2020 - Deep learning for misinformation detection.pdf:application/pdf}
}


@article{Jahan.2023,
 abstract = {With the multiplication of social media platforms, which offer anonymity, easy access and online community formation and online debate, the issue of hate speech detection and tracking becomes a growing challenge to society, individual, policy-makers and researchers. Despite efforts for leveraging automatic techniques for automatic detection and monitoring, their performances are still far from satisfactory, which constantly calls for future research on the issue. This paper provides a systematic review of literature in this field, with a focus on natural language processing and deep learning technologies, highlighting the terminology, processing pipeline, core methods employed, with a focal point on deep learning architecture. From a methodological perspective, we adopt PRISMA guideline of systematic review of the last 10 years literature from ACM Digital Library and Google Scholar. In the sequel, existing surveys, limitations, and future research directions are extensively discussed.},
 author = {Jahan, Md Saroar and Oussalah, Mourad},
 year = {2023},
 title = {{A systematic review of hate speech automatic detection using natural language processing}},
 pages = {126232},
 volume = {546},
 issn = {09252312},
 journal = {{Neurocomputing}},
 doi = {10.1016/j.neucom.2023.126232}
}


@incollection{JarquinVasquez.2023,
 author = {Jarqu{\'i}n-V{\'a}squez, Horacio and Escalante, Hugo Jair and Montes-y-G{\'o}mez, Manuel},
 title = {{Improving the Identification of Abusive Language Through Careful Design of Pre-training Tasks}},
 pages = {283--292},
 volume = {13902},
 publisher = {{Springer Nature Switzerland}},
 isbn = {978-3-031-33782-6},
 series = {{Lecture Notes in Computer Science}},
 editor = {Rodr{\'i}guez-Gonz{\'a}lez, Ansel Yoan and P{\'e}rez-Espinosa, Humberto and Mart{\'i}nez-Trinidad, Jos{\'e} Francisco and Carrasco-Ochoa, Jes{\'u}s Ariel and Olvera-L{\'o}pez, Jos{\'e} Arturo},
 booktitle = {{Pattern Recognition}},
 year = {2023},
 address = {Cham},
 doi = {10.1007/978-3-031-33783-3{\textunderscore }27}
}


@misc{Jin.2020,
 abstract = {Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during fine-tuning. Although these techniques achieve bias reduction for the task and domain at hand, the effects of bias mitigation may not directly transfer to new tasks, requiring additional data collection and customized annotation of sensitive attributes, and re-evaluation of appropriate fairness metrics. We explore the feasibility and benefits of upstream bias mitigation (UBM) for reducing bias on downstream tasks, by first applying bias mitigation to an upstream model through fine-tuning and subsequently using it for downstream fine-tuning. We find, in extensive experiments across hate speech detection, toxicity detection, occupation prediction, and coreference resolution tasks over various bias factors, that the effects of UBM are indeed transferable to new downstream tasks or domains via fine-tuning, creating less biased downstream models than directly fine-tuning on the downstream task or transferring from a vanilla upstream model. Though challenges remain, we show that UBM promises more efficient and accessible bias mitigation in LM fine-tuning.

14 pages; Accepted at NAACL 2021},
 author = {Jin, Xisen and Barbieri, Francesco and Kennedy, Brendan and Davani, Aida Mostafazadeh and Neves, Leonardo and Ren, Xiang},
 date = {2020},
 year = {2020},
 title = {{On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning}},
 keywords = {Computation and Language (cs.CL);FOS: Computer and information sciences;Machine Learning (stat.ML)},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2010.12864}
}


@book{Jones.2020,
 abstract = {Understanding and removing bias from the decisions made by machine learning models is essential to avoid discrimination against unprivileged groups. Despite recent progress in algorithmic fairness, there is still no clear answer as to which bias-mitigation approaches are most effective. Evaluation strategies are typically use-case specific, rely on data with unclear bias, and employ a fixed policy to convert model outputs to decision outcomes. To address these problems, we performed a systematic comparison of a number of popular fairness algorithms applicable to supervised classification. Our study is the most comprehensive of its kind. It utilizes three real and four synthetic datasets, and two different ways of converting model outputs to decisions. It considers fairness, predictive-performance, calibration quality, and speed of 28 different modelling pipelines, corresponding to both fairness-unaware and fairness-aware algorithms. We found that fairness-unaware algorithms typically fail to produce adequately fair models and that the simplest algorithms are not necessarily the fairest ones. We also found that fairness-aware algorithms can induce fairness without material drops in predictive power. Finally, we found that dataset idiosyncracies (e.g., degree of intrinsic unfairness, nature of correlations) do affect the performance of fairness-aware approaches. Our results allow the practitioner to narrow down the approach(es) they would like to adopt without having to know in advance their fairness requirements.},
 author = {Jones, Gareth P. and Hickey, James M. and {Di Stefano}, Pietro G. and Dhanjal, Charanpal and Stoddart, Laura C. and Vasileiou, Vlasios},
 year = {2020},
publisher = {arXiv},
 title = {{Metrics and methods for a systematic comparison of fairness-aware  machine learning algorithms}},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computers and Society;Computer Science - Learning},
 file = {Jones, Hickey et al. 2020 - Metrics and methods:Attachments/Jones, Hickey et al. 2020 - Metrics and methods.pdf:application/pdf}
}


@misc{Joniak.2022,
 abstract = {Language model debiasing has emerged as an important field of study in the NLP community. Numerous debiasing techniques were proposed, but bias ablation remains an unaddressed issue. We demonstrate a novel framework for inspecting bias in pre-trained transformer-based language models via movement pruning. Given a model and a debiasing objective, our framework finds a subset of the model containing less bias than the original model. We implement our framework by pruning the model while fine-tuning it on the debiasing objective. Optimized are only the pruning scores - parameters coupled with the model's weights that act as gates. We experiment with pruning attention heads, an important building block of transformers: we prune square blocks, as well as establish a new way of pruning the entire heads. Lastly, we demonstrate the usage of our framework using gender bias, and based on our findings, we propose an improvement to an existing debiasing method. Additionally, we re-discover a bias-performance trade-off: the better the model performs, the more bias it contains.},
 author = {Joniak, Przemyslaw and Aizawa, Akiko},
 year = {2022},
 title = {{Gender Biases and Where to Find Them: Exploring Gender Bias in  Pre-Trained Transformer-based Language Models Using Movement Pruning}},
 url = {http://arxiv.org/pdf/2207.02463v1},
 keywords = {Computer Science - Computation and Language},
 file = {gender bias and where to find them:Attachments/gender bias and where to find them.pdf:application/pdf}
}


@article{Joshi.2023,
 abstract = {The bias in machine learning models has gained increasing attention in recent years, as these models can reflect and even amplify biases present in the data used to train them. One approach to mitigating bias is identifying and down-weight features that contribute disproportionately to model predictions, which can be accomplished using saliency techniques. Current debiasing methods often lead to the loss of contextual information, where the model tends to respond incorrectly even when the gender information is present in the context; hence, even though the bias reduces, performance (coreference resolution, fluency) also reduces. This paper explores data augmentation and saliency techniques to mitigate bias in natural language generation. Specifically, we explore applying the saliency technique of SHAP (SHapley Additive exPlanations) over a model trained on debiasing using data augmentation (switching gendered words with counterparts) and then applying hard debiasing to remove the influential biased token. We build a dialogue context test setup to evaluate bias and context relevance using the presence of gendered words in the model-generated responses. The response is evaluated based on the gender information from context to ensure the model follows the gender in context. We demonstrate that this approach can effectively reduce the impact of biased features on model predictions while preserving overall model accuracy. Additionally, we discuss potential limitations and future directions for research in this area. Our findings suggest that saliency offers an avenue to address machine learning bias.},
 author = {Joshi, Ratnesh Kumar and Chatterjee, Arindam and Ekbal, Asif},
 year = {2023},
 title = {{Saliency Guided Debiasing: Detecting and mitigating biases in LMs using feature attribution}},
 pages = {126851},
 issn = {09252312},
 journal = {{Neurocomputing}},
 doi = {10.1016/j.neucom.2023.126851}
}


@proceedings{Jurafsky.2020,
 year = {2020},
 title = {{Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}},
 address = {Stroudsburg, PA, USA},
 publisher = {{Association for Computational Linguistics}},
 editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel},
 doi = {10.18653/v1/2020.acl-main}
}


@article{Kamiran.2012,
 abstract = {Recently, the following Discrimination-Aware Classification Problem was

introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. 

This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features.

In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on real-life data.},
 author = {Kamiran, Faisal and Calders, Toon},
 year = {2012},
 title = {{Data preprocessing techniques for classification without discrimination}},
 pages = {1--33},
 volume = {33},
 number = {1},
 issn = {0219-1377},
 journal = {{Knowledge and Information Systems}},
 doi = {10.1007/s10115-011-0463-8},
 file = {Data preprocessing techniques for classification without discrimination:Attachments/Data preprocessing techniques for classification without discrimination.pdf:application/pdf}
}


@article{Kamiran.2018,
 abstract = {Social discrimination is said to occur when an unfavorable decision for an individual is influenced by her membership to certain protected groups such as females and minority ethnic groups. Such discriminatory decisions often exist in historical data. Despite recent works in discrimination-aware data mining, there remains the need for robust, yet easily usable, methods for discrimination control. In this paper, we utilize reject option in classification, a general decision theoretic framework for handling instances whose labels are uncertain, for modeling and controlling discriminatory decisions. Specifically, this framework permits a formal treatment of the intuition that instances close to the decision boundary are more likely to be discriminated in a dataset. Based on this framework, we present three different solutions for discrimination-aware classification. The first solution invokes probabilistic rejection in single or multiple probabilistic classifiers while the second solution relies upon ensemble rejection in classifier ensembles. The third solution integrates one of the first two solutions with situation testing which is a procedure commonly used in the court of law. All solutions are easy to use and provide strong justifications for the decisions. We evaluate our solutions extensively on four real-world datasets and compare their performances with previously proposed discrimination-aware classifiers. The results demonstrate the superiority of our solutions in terms of both performance and flexibility of applicability. In particular, our solutions are effective at removing illegal discrimination from the predictions.},
 author = {Kamiran, Faisal and Mansha, Sameen and Karim, Asim and Zhang, Xiangliang},
 year = {2018},
 title = {{Exploiting reject option in classification for social discrimination control}},
 pages = {18--33},
 volume = {425},
 issn = {00200255},
 journal = {{Information Sciences}},
 doi = {10.1016/j.ins.2017.09.064},
 file = {reject option classification:Attachments/reject option classification.pdf:application/pdf}
}


@article{Kelly.2021,
 author = {Kelly, Anthony},
 year = {2021},
 title = {{A tale of two algorithms: The appeal and repeal of calculated grades systems in England and Ireland in 2020}},
 pages = {725--741},
 volume = {47},
 number = {3},
 issn = {0141-1926},
 journal = {{British Educational Research Journal}},
 doi = {10.1002/berj.3705},
 file = {Kelly 2021 - A tale of two algorithms:Attachments/Kelly 2021 - A tale of two algorithms.pdf:application/pdf}
}


@book{Kennedy.2018,
 abstract = {PDF | The growing prominence of online hate speech is a threat to a safe and just society. This endangering phenomenon requires collaboration across the... | Find, read and cite all the research you need on ResearchGate},
 author = {Kennedy, Brendan and Atari, Mohammad and Davani, Aida Mostafazadeh and Yeh, Leigh and Omrani, Ali and Kim, Yehsong and Coombs, Kris and Havaldar, Shreya and Portillo-Wightman, Gwenyth and Gonzalez, Elaine and Hoover, Joseph and Azatian, Aida and Hussain, Alyzeh and Lara, Austin and olmos, gabriel and Omary, Adam Radwan and Park, Christina and Wijaya, Clarisa and Wang, Xin and Zhang, Yong and Dehghani, Morteza},
 year = {2018},
publisher ={Language Resources and Evaluation},
 title = {{Introducing the Gab Hate Corpus: Defining and applying hate-based rhetoric to social media posts at scale}},
 url = {https://www.researchgate.net/profile/brendan-kennedy-4/publication/346608617_the_gab_hate_corpus_a_collection_of_27k_posts_annotated_for_hate_speech},
 doi = {10.31234/osf.io/hqjxn},
 file = {Kennedy, Atari et al. 2018 - Introducing the Gab Hate Corpus:Attachments/Kennedy, Atari et al. 2018 - Introducing the Gab Hate Corpus.pdf:application/pdf}
}


@book{Kennedy.2020,
 abstract = {We propose a general method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT). We decompose the target construct, hate speech in our case, into multiple constituent components that are labeled as ordinal survey items. Those survey responses are transformed via IRT into a debiased, continuous outcome measure. Our method estimates the survey interpretation bias of the human labelers and eliminates that influence on the generated continuous measure. We further estimate the response quality of each labeler using faceted IRT, allowing responses from low-quality labelers to be removed.  Our faceted Rasch scaling procedure integrates naturally with a multitask deep learning architecture for automated prediction on new data. The ratings on the theorized components of the target outcome are used as supervised, ordinal variables for the neural networks' internal concept learning. We test the use of an activation function (ordinal softmax) and loss function (ordinal cross-entropy) designed to exploit the structure of ordinal outcome variables. Our multitask architecture leads to a new form of model interpretation because each continuous prediction can be directly explained by the constituent components in the penultimate layer.  We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers to measure a continuous spectrum from hate speech to counterspeech. We evaluate Universal Sentence Encoders, BERT, and RoBERTa as language representation models for the comment text, and compare our predictive accuracy to Google Jigsaw's Perspective API models, showing significant improvement over this standard benchmark.},
 author = {Kennedy, Chris J. and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia},
 year = {2020},
publisher = {arXiv},
 title = {{Constructing interval variables via faceted Rasch measurement and  multitask deep learning: a hate speech application}},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning},
 file = {Kennedy, Bacon et al. 2020 - Constructing interval variables via faceted:Attachments/Kennedy, Bacon et al. 2020 - Constructing interval variables via faceted.pdf:application/pdf}
}


@book{Kennedy.2020b,
 abstract = {Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like {\textquotedbl}gay{\textquotedbl} or {\textquotedbl}black{\textquotedbl} are used in offensive or prejudiced ways. Such biases manifest in false positives when these identifiers are present, due to models' inability to learn the contexts which constitute a hateful usage of identifiers. We extract SOC post-hoc explanations from fine-tuned BERT classifiers to efficiently detect bias towards identity terms. Then, we propose a novel regularization technique based on these explanations that encourages models to learn from the context of group identifiers in addition to the identifiers themselves. Our approach improved over baselines in limiting false positives on out-of-domain data while maintaining or improving in-domain performance. Project page: https://inklab.usc.edu/contextualize-hate-speech/.},
 author = {Kennedy, Brendan and Jin, Xisen and Davani, Aida Mostafazadeh and Dehghani, Morteza and Ren, Xiang},
 year = {2020},
publisher = {Association for Computational Linguistics},
title = {{Contextualizing Hate Speech Classifiers with Post-hoc Explanation}},
 keywords = {Computer Science - Computation and Language;Computer Science - Information Retrieval;Computer Science - Learning},
 file = {Kennedy, Jin et al. 2020 - Contextualizing Hate Speech Classifiers:Attachments/Kennedy, Jin et al. 2020 - Contextualizing Hate Speech Classifiers.pdf:application/pdf}
}


@article{Khan.2023,
 abstract = {Social media platforms are heavily used by people to express their views in their native languages. Besides positive views, people often use abusive or offensive language to express their anger or frustration. Resource-rich languages have offensive language detection systems which automatically monitor and block offensive content, however, they are very rare for low-resourced languages. This is because of the nonavailability of datasets for local languages. This article proposes a model which automatically detects offensive language for a very low-resource language, i.e., Pashto. The Roman Pashto dataset is created by picking 60 thousand comments from different social media and labeling them manually. The proposed model is trained and tested using three different feature extraction approaches, i.e., bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and sequence integer encoding. Four traditional classifiers and a deep sequence model are used to train on this task. Experimental result shows that the random forest classifier works best and give 94.07 {\%} testing accuracy on a combination of unigrams, bigrams, and trigrams. The same classifier gives maximum accuracy of 93.90 {\%} with TF-IDF. However, the overall highest testing accuracy of 97.21{\%} is achieved by using bidirectional long short-term memory (BLSTM). The corpus created in this work is made available for the researcher working in this domain.},
 author = {Khan, Anas Ali and Iqbal, M. Hammad and Nisar, Shibli and Ahmad, Awais and Iqbal, Waseem},
 year = {2023},
 title = {{Offensive Language Detection for Low Resource Language Using Deep Sequence Model}},
 pages = {1--9},
 issn = {2329-924X},
 journal = {{IEEE Transactions on Computational Social Systems}},
 doi = {10.1109/tcss.2023.3280952}
}


@proceedings{Kittler.2001,
 year = {2001},
 title = {{Multiple classifier systems: Second international workshop, Cambridge, UK, July 2 - 4, 2001 ; proceedings}},
 address = {Berlin},
 edition = {[Elektronische Ressource]},
 volume = {2096},
 publisher = {Springer},
 isbn = {978-3-540-48219-2},
 series = {{Lecture Notes in Computer Science}},
 editor = {Kittler, Josef},
 institution = {MCS},
 doi = {10.1007/3-540-48219-9}
}


@book{Kleinberg.2016,
 abstract = {Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously. Moreover, even satisfying all three conditions approximately requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them.},
 author = {Kleinberg, Jon and Mullainathan, Sendhil and Raghavan, Manish},
 year = {2016},
 title = {{Inherent Trade-Offs in the Fair Determination of Risk Scores}},
 keywords = {Computer Science - Computers and Society;Computer Science - Learning;Statistics - Machine Learning},
 file = {Kleinberg, Mullainathan et al. 2016 - Inherent Trade-Offs in the Fair:Attachments/Kleinberg, Mullainathan et al. 2016 - Inherent Trade-Offs in the Fair.pdf:application/pdf}
}


@article{Kohatsu.2019,
 abstract = {Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet's word, emoji, and expression tokens' embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literature.},
 author = {Pereira-Kohatsu, Juan Carlos and Quijano-S{\'a}nchez, Lara and Liberatore, Federico and Camacho-Collados, Miguel},
 year = {2019},
 title = {{Detecting and Monitoring Hate Speech in Twitter}},
 volume = {19},
 number = {21},
 journal = {{Sensors (Basel, Switzerland)}},
 doi = {10.3390/s19214654},
 file = {detecting and monitoring hate speech in twitter:Attachments/detecting and monitoring hate speech in twitter.pdf:application/pdf}
}


@inproceedings{Kolla.2023,
 author = {Kolla, Manideep and Savadamuthu, Aravinth},
 title = {{The Impact of Racial Distribution in Training Data on Face Recognition Bias: A Closer Look}},
 publisher = {IEEE},
 booktitle = {{2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)}},
 year = {2023},
 doi = {10.1109/wacvw58289.2023.00035},
 file = {Kolla, Savadamuthu 2023 - The Impact of Racial Distribution:Attachments/Kolla, Savadamuthu 2023 - The Impact of Racial Distribution.pdf:application/pdf}
}


@inproceedings{Krasanakis.2018,
 abstract = {Machine learning bias and fairness have recently emerged as key issues due to the pervasive deployment of data-driven decision making in a variety of sectors and services. It has often been argued that unfair classifications can be attributed to bias in training data, but previous attempts to ``repair'' training data have led to limited success. To circumvent shortcomings prevalent in data repairing approaches, such as those that weight training samples of the sensitive group (e.g. gender, race, financial status) based on their misclassification error, we present a process that iteratively adapts training sample weights with a theoretically grounded model. This model addresses different kinds of bias to better achieve fairness objectives, such as trade-offs between accuracy and disparate impact elimination or disparate mistreatment elimination. We show that, compared to previous fairness-aware approaches, our methodology achieves better or similar trades-offs between accuracy and unfairness mitigation on real-world and synthetic datasets.},
 author = {Krasanakis, Emmanouil and Spyromitros-Xioufis, Eleftherios and Papadopoulos, Symeon and Kompatsiaris, Yiannis},
 title = {{Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification}},
 pages = {853--862},
 publisher = {{ACM Press}},
 isbn = {9781450356398},
 editor = {Champin, Pierre-Antoine and Gandon, Fabien and Lalmas, Mounia and Ipeirotis, Panagiotis G.},
 booktitle = {{Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18}},
 year = {2018},
 address = {New York, New York, USA},
 doi = {10.1145/3178876.3186133},
 file = {adaptive sensitive reweighting:Attachments/adaptive sensitive reweighting.pdf:application/pdf}
}


@proceedings{Ku.2017,
 year = {2017},
 title = {{Proceedings of the Fifth International Workshop on Natural Language  Processing for Social Media}},
 address = {Stroudsburg, PA, USA},
 publisher = {{Association for Computational Linguistics}},
 editor = {Ku, Lun-Wei and Li, Cheng-Te},
 doi = {10.18653/v1/W17-11}
}


@book{Kuhl.2023,
 abstract = {Counterfactual explanations (CFEs) are a popular approach in explainable artificial intelligence (xAI), highlighting changes to input data necessary for altering a model's output. A CFE can either describe a scenario that is better than the factual state (upward CFE), or a scenario that is worse than the factual state (downward CFE). However, potential benefits and drawbacks of the directionality of CFEs for user behavior in xAI remain unclear. The current user study (N=161) compares the impact of CFE directionality on behavior and experience of participants tasked to extract new knowledge from an automated system based on model predictions and CFEs. Results suggest that upward CFEs provide a significant performance advantage over other forms of counterfactual feedback. Moreover, the study highlights potential benefits of mixed CFEs improving user performance compared to downward CFEs or no explanations. In line with the performance results, users' explicit knowledge of the system is statistically higher after receiving upward CFEs compared to downward comparisons. These findings imply that the alignment between explanation and task at hand, the so-called regulatory fit, may play a crucial role in determining the effectiveness of model explanations, informing future research directions in xAI. To ensure reproducible research, the entire code, underlying models and user data of this study is openly available: https://github.com/ukuhl/DirectionalAlienZoo},
 author = {Kuhl, Ulrike and Artelt, Andr{\'e} and Hammer, Barbara},
 year = {2023},
publisher = {Springer, Cham},
 title = {{For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI}},
 volume = {1903},
 doi = {10.1007/978-3-031-44070-0{\textunderscore }14},
 file = {Kuhl, Artelt et al. 2023 - For Better or Worse:Attachments/Kuhl, Artelt et al. 2023 - For Better or Worse.pdf:application/pdf}
}


@misc{Kumar.2023,
 abstract = {Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial training) impose debiasing by updating a model's parameters, effectively transferring the model to a new, irreversible debiased state. In this work, we propose a novel approach to develop stand-alone debiasing functionalities separate from the model, which can be integrated into the model on-demand, while keeping the core model untouched. Drawing from the concept of AdapterFusion in multi-task learning, we introduce DAM (Debiasing with Adapter Modules) - a debiasing approach to first encapsulate arbitrary bias mitigation functionalities into separate adapters, and then add them to the model on-demand in order to deliver fairness qualities. We conduct a large set of experiments on three classification tasks with gender, race, and age as protected attributes. Our results show that DAM improves or maintains the effectiveness of bias mitigation, avoids catastrophic forgetting in a multi-attribute scenario, and maintains on-par task performance, while granting parameter-efficiency and easy switching between the original and debiased models.

Accepted at EACL 2023},
 author = {Kumar, Deepak and Lesota, Oleg and Zerveas, George and Cohen, Daniel and Eickhoff, Carsten and Schedl, Markus and Rekabsaz, Navid},
 date = {2023},
 title = {{Parameter-efficient Modularised Bias Mitigation via AdapterFusion}},
 keywords = {Artificial Intelligence (cs.AI);Computation and Language (cs.CL);FOS: Computer and information sciences},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2302.06321}
}


@article{LaPenaSarracen.2023,
 abstract = {Hate speech detection refers broadly to the automatic identification of language that may be considered discriminatory against certain groups of people. The goal is to help online platforms to identify and remove harmful content. Humans are usually capable of detecting hatred in critical cases, such as when the hatred is non-explicit, but how do computer models address this situation? In this work, we aim to contribute to the understanding of ethical issues related to hate speech by analysing two transformer-based models trained to detect hate speech. Our study focuses on analysing the relationship between these models and a set of hateful keywords extracted from the three well-known datasets. For the extraction of the keywords, we propose a metric that takes into account the division among classes to favour the most common words in hateful contexts. In our experiments, we first compared the overlap between the extracted keywords with the words to which the models pay the most attention in decision-making. On the other hand, we investigate the bias of the models towards the extracted keywords. For the bias analysis, we characterize and use two metrics and evaluate two strategies to try to mitigate the bias. Surprisingly, we show that over 50{\%} of the salient words of the models are not hateful and that there is a higher number of hateful words among the extracted keywords. However, we show that the models appear to be biased towards the extracted keywords. Experimental results suggest that fitting models with hateful texts that do not contain any of the keywords can reduce bias and improve the performance of the models.},
 author = {de {La Pe{\~n}a Sarrac{\'e}n}, Gretel Liz and Rosso, Paolo},
 year = {2023},
 title = {{Systematic keyword and bias analyses in hate speech detection}},
 pages = {103433},
 volume = {60},
 number = {5},
 issn = {03064573},
 journal = {{Information Processing {\&} Management}},
 doi = {10.1016/j.ipm.2023.103433}
}


@article{Li.2022,
 author = {Li, Yunyi and De-Arteaga, Maria and Saar-Tsechansky, Maytal},
 year = {2022},
 title = {{When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias}},
 pages = {133--146},
 volume = {10},
 number = {1},
 issn = {2769-1330},
 journal = {{Proceedings of the AAAI Conference on Human Computation and Crowdsourcing}},
 doi = {10.1609/hcomp.v10i1.21994},
 file = {Li, De-Arteaga et al. 2022 - When More Data Lead Us:Attachments/Li, De-Arteaga et al. 2022 - When More Data Lead Us.pdf:application/pdf}
}


@misc{Li.2023,
 abstract = {Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular, human-generated labels are prone to encoding societal biases. While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias. After illustrating its performance on a synthetic dataset, we apply DeCoLe in the context of hate speech detection, where label bias has been recognized as an important challenge, and show that it successfully identifies biased labels and outperforms competing approaches.},
 author = {Li, Yunyi and De-Arteaga, Maria and Saar-Tsechansky, Maytal},
 date = {2023},
year = {2023},
 title = {{Mitigating Label Bias via Decoupled Confident Learning}},
 url = {http://arxiv.org/pdf/2307.08945v1},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning},
 file = {Li, De-Arteaga et al 18072023 - Mitigating Label Bias via Decoupled:Attachments/Li, De-Arteaga et al 18072023 - Mitigating Label Bias via Decoupled.pdf:application/pdf}
}


@article{Lieberman.2020,
 abstract = {For hundreds of thousands of years, humans only communicated in person, but in just the past fifty years they have started also communicating online. Today, people communicate more online than offline. What does this shift mean for human social life? We identify four structural differences between online (versus offline) interaction: (1) fewer nonverbal cues, (2) greater anonymity, (3) more opportunity to form new social ties and bolster weak ties, and (4) wider dissemination of information. Each of these differences underlies systematic psychological and behavioral consequences. Online and offline lives often intersect; we thus further review how online engagement can (1) disrupt or (2) enhance offline interaction. This work provides a useful framework for studying the influence of technology on social life (119/120).},
 author = {Lieberman, Alicea and Schroeder, Juliana},
 year = {2020},
 title = {{Two social lives: How differences between online and offline interaction influence social outcomes}},
 keywords = {Communication;Humans;Social Behavior;Social Interaction;Social Media},
 pages = {16--21},
 volume = {31},
 issn = {2352-250X},
 journal = {{Current Opinion in Psychology}},
 doi = {10.1016/j.copsyc.2019.06.022},
 file = {Lieberman, Schroeder 2020 - Two social lives:Attachments/Lieberman, Schroeder 2020 - Two social lives.pdf:application/pdf}
}


@article{Lindsay.2020,
 abstract = {Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.},
 author = {Lindsay, Grace W.},
 year = {2020},
 title = {{Attention in Psychology, Neuroscience, and Machine Learning}},
 url = {https://www.frontiersin.org/articles/10.3389/fncom.2020.00029/full?trk=public_post_comment-text},
 keywords = {artificial neural networks;Attention;Awareness;Machine Learning;Memory;Vision},
 pages = {29},
 volume = {14},
 issn = {1662-5188},
 journal = {{Frontiers in Computational Neuroscience}},
 doi = {10.3389/fncom.2020.00029},
 file = {Lindsay 2020 - Attention in Psychology:Attachments/Lindsay 2020 - Attention in Psychology.pdf:application/pdf}
}


@proceedings{Liu.2019,
 year = {2019},
 title = {{The World Wide Web Conference}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9781450366748},
 editor = {Liu, Ling and White, Ryen},
 doi = {10.1145/3308558}
}


@misc{Liu.2022,
 abstract = {The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noised-based data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments.},
 author = {Liu, Xiaogeng and Wang, Haoyu and Zhang, Yechao and Wu, Fangzhou and Hu, Shengshan},
 date = {2022},
 title = {{Towards Efficient Data-Centric Robust Machine Learning with Noise-based  Augmentation}},
 url = {http://arxiv.org/pdf/2203.03810},
 keywords = {Artificial Intelligence (cs.AI);Computer Science - Artificial Intelligence;Computer Science - Cryptography and Security;Computer Science - Learning;Cryptography and Security (cs.CR);Machine Learning (cs.LG)},
 file = {Liu, Wang et al. 08.03.2022 - Towards Efficient Data-Centric Robust Machine:Attachments/Liu, Wang et al. 08.03.2022 - Towards Efficient Data-Centric Robust Machine.pdf:application/pdf}
}


@inproceedings{Liu.2023,
 abstract = {Group fairness is a popular approach to prevent unfavorable treatment of individuals based on sensitive attributes such as race, gender, and disability. However, the reliance of group fairness on access to discrete group information raises several limitations and concerns, especially with regard to privacy, intersectionality, and unforeseen biases. In this work, we propose a {\grqq}group-free{\textquotedbl} measure of fairness that does not rely on sensitive attributes and, instead, is based on homophily in social networks, i.e., the common property that individuals sharing similar attributes are more likely to be connected. Our measure is group-free as it avoids recovering any form of group memberships and uses only pairwise similarities between individuals to define inequality in outcomes relative to the homophily structure in the network. We theoretically justify our measure by showing it is commensurate with the notion of additive decomposability in the economic inequality literature and also bound the impact of non-sensitive confounding attributes. Furthermore, we apply our measure to develop fair algorithms for classification, maximizing information access, and recommender systems. Our experimental results show that the proposed approach can reduce inequality among protected classes without knowledge of sensitive attribute labels. We conclude with a discussion of the limitations of our approach when applied in real-world settings.},
 author = {Liu, David and Do, Virginie and Usunier, Nicolas and Nickel, Maximilian},
 title = {{Group fairness without demographics using social networks}},
 pages = {1432--1449},
 publisher = {ACM},
 isbn = {9798400701924},
 booktitle = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3593013.3594091},
 file = {Group fairness without demographics using social networks:Attachments/Group fairness without demographics using social networks.pdf:application/pdf}
}


@inproceedings{Lohia.2019,
 abstract = {Whereas previous post-processing approaches for increasing the

fairness of predictions of biased classifiers address only group fair-

ness, we propose a method for increasing both individual and group

fairness. Our novel framework includes an individual bias detector

used to prioritize data samples in a bias mitigation algorithm aiming

to improve the group fairness measure of disparate impact. We show

superior performance to previous work in the combination of classi-

fication accuracy, individual fairness and group fairness on several

real-world datasets in applications such as credit, employment, and

criminal justice.},
 author = {Lohia, Pranay K. and {Natesan Ramamurthy}, Karthikeyan and Bhide, Manish and Saha, Diptikalyan and Varshney, Kush R. and Puri, Ruchir},
 title = {{Bias Mitigation Post-processing for Individual and Group Fairness}},
 pages = {2847--2851},
 publisher = {IEEE},
 isbn = {978-1-4799-8131-1},
 booktitle = {{ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
 year = {2019},
 doi = {10.1109/ICASSP.2019.8682620},
 file = {bias mitigation post-processing for individual and group fairness:Attachments/bias mitigation post-processing for individual and group fairness.pdf:application/pdf}
}


@proceedings{Longo.2023,
 abstract = {xAI for time series and Natural Language Processing -- Human-centered explanations and xAI for Trustworthy and Responsible AI -- Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.



This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine. Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems. Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.},
 year = {2023},
 title = {{Explainable Artificial Intelligence: First World Conference, xAI 2023, Lisbon, Portugal, July 26--28, 2023, Proceedings, Part III}},
 keywords = {Application software;Artificial Intelligence;Computer networks;Natural language processing (Computer science)},
 address = {Cham},
 edition = {1st ed. 2023},
 volume = {1903},
 publisher = {{Springer Nature Switzerland} and {Imprint Springer}},
 isbn = {978-3-031-44070-0},
 series = {{Communications in Computer and Information Science}},
 editor = {Longo, Luca},
 doi = {10.1007/978-3-031-44070-0}
}


@article{Lunardon.2014,
 author = {Lunardon, Nicola and Menardi, Giovanna and Torelli, Nicola},
 year = {2014},
 title = {{ROSE: a Package for Binary Imbalanced Learning}},
 pages = {79},
 volume = {6},
 number = {1},
 issn = {2073-4859},
 journal = {{The R Journal}},
 doi = {10.32614/rj-2014-008},
 file = {Lunardon, Menardi et al. 2014 - ROSE a Package for Binary:Attachments/Lunardon, Menardi et al. 2014 - ROSE a Package for Binary.pdf:application/pdf}
}


@misc{Mahabadi.2019,
 abstract = {Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models that fail to generalize to out-of-domain datasets and are likely to perform poorly in real-world scenarios. We propose two learning strategies to train neural models, which are more robust to such biases and transfer better to out-of-domain datasets. The biases are specified in terms of one or more bias-only models, which learn to leverage the dataset biases. During training, the bias-only models' predictions are used to adjust the loss of the base model to reduce its reliance on biases by down-weighting the biased examples and focusing the training on the hard examples. We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data. Results show that our debiasing methods greatly improve robustness in all settings and better transfer to other textual entailment datasets. Our code and data are publicly available in \url{https://github.com/rabeehk/robust-nli}.

Accepted in ACL 2020 as a long paper},
 author = {Mahabadi, Rabeeh Karimi and Belinkov, Yonatan and Henderson, James},
 date = {2019},
 title = {{End-to-End Bias Mitigation by Modelling Biases in Corpora}},
 keywords = {Computation and Language (cs.CL);FOS: Computer and information sciences},
 publisher = {arXiv},
 doi = {10.48550/arXiv.1909.06321},
 file = {modeling bias in corpara:Attachments/modeling bias in corpara.pdf:application/pdf}
}


@inproceedings{Majumdar.2021,
 abstract = {Due to the large applicability of AI systems in various applications, fairness in model predictions is extremely important to ensure that the systems work equally well for everyone. Biased feature representations might often lead to

unfair model predictions. To address the concern, in this research, a novel method, termed as Attention Aware Debiasing (AAD) method, is proposed to learn unbiased feature representations. The proposed method uses an atten-

tion mechanism to focus on the features important for the main task while suppressing the features related to the sensitive attributes. This minimizes the model's dependency on the sensitive attribute while performing the main task. Multiple experiments are performed on two publicly available

datasets, MORPH and UTKFace, to showcase the effectiveness of the proposed AAD method for bias mitigation. The proposed AAD method enhances the overall model performance and reduces the disparity in model prediction across different subgroups.},
 author = {Majumdar, Puspita and Singh, Richa and Vatsa, Mayank},
 title = {{Attention Aware Debiasing for Unbiased Model Prediction}},
 pages = {4116--4124},
 publisher = {IEEE},
 isbn = {978-1-6654-0191-3},
 booktitle = {{2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}},
 year = {2021},
 doi = {10.1109/ICCVW54120.2021.00459},
 file = {Majumdar{\_}Attention{\_}Aware{\_}Debiasing{\_}for{\_}Unbiased{\_}Model{\_}Prediction{\_}ICCVW{\_}2021{\_}paper:Attachments/Majumdar{\_}Attention{\_}Aware{\_}Debiasing{\_}for{\_}Unbiased{\_}Model{\_}Prediction{\_}ICCVW{\_}2021{\_}paper.pdf:application/pdf}
}


@incollection{Mandhala.2023,
 abstract = {Bias in the data always affects the performance of the system. Our need of finding the accurate results in a system using any Machine Learning algorithms will be degraded when bias is present in the data that we choose to analyze. Mitigating bias in Artificial...},
 author = {Mandhala, Venkata Naresh and Bhattacharyya, Debnath and Midhunchakkaravarthy, Divya},
 title = {{A Novel Study on Tools and Frameworks for Mitigating Bias in Multimodal Datasets}},
 url = {https://link.springer.com/chapter/10.1007/978-981-19-2358-6_27},
 pages = {277--283},
 publisher = {{Springer, Singapore}},
 booktitle = {{Proceedings of the International Conference on Cognitive and Intelligent Computing}},
 year = {2023},
 doi = {10.1007/978-981-19-2358-6{\textunderscore }27},
 file = {Mandhala, Bhattacharyya et al. 2023 - A Novel Study on Tools:Attachments/Mandhala, Bhattacharyya et al. 2023 - A Novel Study on Tools.pdf:application/pdf}
}


@incollection{MargotE.Kaminski.2021,
 abstract = {Many have called for algorithmic accountability: laws governing decision-making by complex algorithms, or AI. The EU's General Data Protection Regulation (GDPR) now establishes exactly this. The recent debate over the right to explanation (a right to information about individual decisions made by algorithms) has obscured the significant algorithmic accountability regime established by the GDPR. The GDPR's provisions on algorithmic accountability, which include a right to explanation, have the potential to be broader, stronger, and deeper than the preceding requirements of the Data Protection Directive. This Essay clarifies, largely for a U.S. audience, what the GDPR actually requires, incorporating recently released authoritative guidelines},
 author = {{Margot E. Kaminski}},
 title = {{The right to explanation, explained}},
 url = {https://www.elgaronline.com/edcollchap/edcoll/9781788119917/9781788119917.00024.xml},
 pages = {278--299},
 publisher = {{Edward Elgar Publishing Limited}},
 isbn = {9781788119924},
 series = {{Research handbooks in information law series}},
 editor = {Sandeen, Sharon K. and Rademacher, Christoph W. and Ohly, Ansgar},
 booktitle = {{Research handbook on information law and governance}},
 year = {2021},
 address = {Cheltenham, UK and Northampton, Massachusetts},
 doi = {10.4337/9781788119924.00024}
}


@article{Mathew.2021,
 abstract = {Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public for other researchers.},
 author = {Mathew, Binny and Saha, Punyajoy and Yimam, Seid Muhie and Biemann, Chris and Goyal, Pawan and Mukherjee, Animesh},
 year = {2021},
 title = {{HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection}},
 url = {https://ojs.aaai.org/index.php/AAAI/article/view/17745},
 keywords = {Computational Social Science;Fairness and Equality;Justice;Other Social Impact;Social Welfare},
 pages = {14867--14875},
 volume = {35},
 number = {17},
 issn = {2159-5399},
 journal = {{Proceedings of the AAAI Conference on Artificial Intelligence}},
 doi = {10.1609/aaai.v35i17.17745},
 file = {17745-Article Text-21239-1-2-20210518-1:Attachments/17745-Article Text-21239-1-2-20210518-1.pdf:application/pdf}
}


@article{Matusitz.2007,
 author = {Matusitz, Jonathan},
 year = {2007},
 title = {{The implications of the Internet for human communication}},
 url = {https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=2a6c86bf4762281f5a492ce03e94c8c0860a3453},
 pages = {21--34},
 number = {7},
 journal = {{Journal of Information Technology Impact}},
 file = {The implications of the Internet 2007:Attachments/The implications of the Internet 2007.pdf:application/pdf}
}


@article{Mazari.2023,
 abstract = {The social media world nowadays is overwhelmed with unfiltered content ranging from cyberbullying and cyberstalking to hate speech. Therefore, identifying and cleaning up such toxic language presents a big challenge and an active area of research. This study is dedicated to multi-aspect hate speech detection based on classifying text in multi-labels including `identity hate', `threat', `insult', `obscene', `toxic' and `severe toxic'. The proposed approach is based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model combined with Deep Learning (DL) models to compose several ensemble learning architectures. The DL models used are built by stacking Bidirectional Long-Short Term Memory (Bi-LSTM) and/or Bidirectional Gated Recurrent Unit (Bi-GRU) on GloVe and FastText word embeddings. Whereby, these models and BERT are trained individually on multi-label hateful dataset and used in combination for hate speech detection tasks on social media. Thus, we demonstrate that encoding texts by using recent word embedding techniques as FastText and GloVe alongside Bi-LSTM and Bi-GRU can create models that, when combined with BERT, can enhance the ROC-AUC score to 98.63{\%}.},
 author = {Mazari, Ahmed Cherif and Boudoukhani, Nesrine and Djeffal, Abdelhamid},
 year = {2023},
 title = {{BERT-based ensemble learning for multi-aspect hate speech detection}},
 url = {https://link.springer.com/article/10.1007/s10586-022-03956-x},
 keywords = {Computer Communication Networks;Operating Systems;Processor Architectures},
 pages = {1--15},
 issn = {1573-7543},
 journal = {{Cluster Computing}},
 doi = {10.1007/s10586-022-03956-x},
 file = {Mazari, Boudoukhani et al. 2023 - BERT-based ensemble learning for multi-aspect:Attachments/Mazari, Boudoukhani et al. 2023 - BERT-based ensemble learning for multi-aspect.pdf:application/pdf}
}


@article{Mehrabi.2022,
 abstract = {With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of suc...},
 author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram},
 year = {2022},
 title = {{A Survey on Bias and Fairness in Machine Learning}},
 keywords = {Deep Learning;Fairness and bias in artificial intelligence;Machine Learning;natural language processing;representation learning},
 pages = {1--35},
 volume = {54},
 number = {6},
 issn = {0360-0300},
 journal = {{ACM Computing Surveys}},
 doi = {10.1145/3457607},
 file = {Mehrabi, Morstatter et al. 2022 - A Survey on Bias:Attachments/Mehrabi, Morstatter et al. 2022 - A Survey on Bias.pdf:application/pdf}
}


@inproceedings{Meyer.2023,
 abstract = {We introduce dataset multiplicity, a way to study how inaccuracies, uncertainty, and social bias in training datasets impact test-time predictions. The dataset multiplicity framework asks a counterfactual question of what the set of resultant models (and associated test-time predictions) would be if we could somehow access all hypothetical, unbiased versions of the dataset. We discuss how to use this framework to encapsulate various sources of uncertainty in datasets' factualness, including systemic social bias, data collection practices, and noisy labels or features. We show how to exactly analyze the impacts of dataset multiplicity for a specific model architecture and type of uncertainty: linear models with label errors. Our empirical analysis shows that real-world datasets, under reasonable assumptions, contain many test samples whose predictions are affected by dataset multiplicity. Furthermore, the choice of domain-specific dataset multiplicity definition determines what samples are affected, and whether different demographic groups are disparately impacted. Finally, we discuss implications of dataset multiplicity for machine learning practice and research, including considerations for when model outcomes should not be trusted.},
 author = {Meyer, Anna P. and Albarghouthi, Aws and D'Antoni, Loris},
 title = {{The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions}},
 pages = {193--204},
 publisher = {ACM},
 isbn = {9798400701924},
 booktitle = {{2023 ACM Conference on Fairness, Accountability, and Transparency}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3593013.3593988},
 file = {The Dataset Multiplicity Problem How Unreliable Data Imp:Attachments/The Dataset Multiplicity Problem How Unreliable Data Imp.pdf:application/pdf}
}


@misc{Miconi.2017,
 abstract = {Various measures can be used to estimate bias or unfairness in a predictor. Previous work has already established that some of these measures are incompatible with each other. Here we show that, when groups differ in prevalence of the predicted event, several intuitive, reasonable measures of fairness (probability of positive prediction given occurrence or non-occurrence; probability of occurrence given prediction or non-prediction; and ratio of predictions over occurrences for each group) are all mutually exclusive: if one of them is equal among groups, the other two must differ. The only exceptions are for perfect, or trivial (always-positive or always-negative) predictors. As a consequence, any non-perfect, non-trivial predictor must necessarily be {\textquotedbl}unfair{\textquotedbl} under two out of three reasonable sets of criteria. This result readily generalizes to a wide range of well-known statistical quantities (sensitivity, specificity, false positive rate, precision, etc.), all of which can be divided into three mutually exclusive groups. Importantly, The results applies to all predictors, whether algorithmic or human. We conclude with possible ways to handle this effect when assessing and designing prediction methods.},
 author = {Miconi, Thomas},
 date = {2017},
 title = {{The impossibility of {\textquotedbl}fairness{\textquotedbl}: a generalized impossibility result for decisions}},
 url = {https://arxiv.org/pdf/1707.01195},
 keywords = {Applications (stat.AP);Artificial Intelligence (cs.AI);Machine Learning (stat.ML)},
 file = {The impossibility of fairness a generalized impossibilit:Attachments/The impossibility of fairness a generalized impossibilit.pdf:application/pdf}
}


@misc{Molamohammadi.11.06.2023,
 abstract = {The growing utilization of machine learning (ML) in decision-making processes raises questions about its benefits to society. In this study, we identify and analyze three axes of heterogeneity that significantly influence the trajectory of ML products. These axes are i) values, culture and regulations, ii) data composition, and iii) resource and infrastructure capacity. We demonstrate how these axes are interdependent and mutually influence one another, emphasizing the need to consider and address them jointly. Unfortunately, the current research landscape falls short in this regard, often failing to adopt a holistic approach. We examine the prevalent practices and methodologies that skew these axes in favor of a selected few, resulting in power concentration, homogenized control, and increased dependency. We discuss how this fragmented study of the three axes poses a significant challenge, leading to an impractical solution space that lacks reflection of real-world scenarios. Addressing these issues is crucial to ensure a more comprehensive understanding of the interconnected nature of society and to foster the democratic and inclusive development of ML systems that are more aligned with real-world complexities and its diverse requirements.},
 author = {Molamohammadi, Maryam and Taik, Afaf and {Le Roux}, Nicolas and Farnadi, Golnoosh},
 date = {11.06.2023},
 title = {{Unraveling the Interconnected Axes of Heterogeneity in Machine Learning  for Democratic and Inclusive Advancements}},
 url = {http://arxiv.org/pdf/2306.10043v1},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computers and Society;Computer Science - Learning}
}


@article{Mozafari.2020,
 abstract = {Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE), respectively. The results show the existence of systematic racial bias in trained classifiers, as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned group. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems.},
 author = {Mozafari, Marzieh and Farahbakhsh, Reza and Crespi, No{\"e}l},
 year = {2020},
 title = {{Hate speech detection and racial bias mitigation in social media based on BERT model}},
 keywords = {Algorithms;Databases as Topic;Deep Learning;Female;Hate;Humans;Male;Models, Theoretical;Racism;Sexism;Social Media;Speech},
 pages = {e0237861},
 volume = {15},
 number = {8},
 journal = {{PloS one}},
 doi = {10.1371/journal.pone.0237861},
 file = {morafari bias mitigation with bert:Attachments/morafari bias mitigation with bert.pdf:application/pdf}
}


@article{Mozafari.2022,
 abstract = {Automatic detection of abusive online content such as hate speech, offensive language, threats,etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English. However, detecting hatred and abuse in low-resource languages is a non-trivial challenge.

The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases. We propose a meta learning-based approach to study the problem of few-shot hate speech and offensive language detection in low-resource languages that will allow hateful or offensive content to be predicted by only observing a few labeled data items in a specific target language.

We investigate the feasibility of applying a meta learning approach in cross-lingual few-shot hate speech detection by leveraging two meta learning models based on optimization-based and metric-based (MAML and Proto-MAML) methods. To the best of our knowledge, this is the first effort of this kind. To evaluate the performance of our approach, we consider hate speech and offensive language detection as two separate tasks and make two diverse collections of different publicly available datasets comprising 15 datasets across 8 languages for hate speech and 6 datasets across 6 languages for offensive language. Our experiments show that meta learning-based models outperform transfer learning-based models in a majority of cases, and that Proto-MAML is the best performing model, as it can quickly generalize and adapt to new languages with only a few labeled data points (generally, 16 samples per class yields an effective performance) to identify hateful or offensive content.},
 author = {Mozafari, Marzieh and Farahbakhsh, Reza and Crespi, Noel},
 year = {2022},
 title = {{Cross-Lingual Few-Shot Hate Speech and Offensive Language Detection Using Meta Learning}},
 pages = {14880--14896},
 volume = {10},
 journal = {{IEEE Access}},
 doi = {10.1109/ACCESS.2022.3147588}
}


@article{Nascimento.2022,
 abstract = {Hate speech on online social media platforms is now at a level that has been considered a serious concern by governments, media outlets, and scientists, especially because it is easily spread, promoting harm to individuals

and society, and made it virtually impossible to tackle with using just human analysis. Automatic approaches using machine learning and natural language processing are helpful for detection. For such applications, amongst several different approaches, it is essential to investigate the systems' robustness to deal with biases towards identity terms (gender, race, religion, for example). In this work, we analyse gender bias in different datasets and proposed a ensemble learning approach based on different feature spaces for hate speech detection with the aim that the model can learn from different abstractions of the problem, namely unintended bias evaluation metrics. 

We have used nine different feature spaces to train the pool of classifiers and evaluated our approach on a publicly available corpus, and our results demonstrate its effectiveness compared to state-of-the-art solutions.},
 author = {Nascimento, Francimaria R.S. and Cavalcanti, George D.C. and {Da Costa-Abreu}, M{\'a}rjory},
 year = {2022},
 title = {{Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on social media using ensemble learning}},
 pages = {117032},
 volume = {201},
 issn = {09574174},
 journal = {{Expert Systems with Applications}},
 doi = {10.1016/j.eswa.2022.117032},
 file = {unintended bias evaluation:Attachments/unintended bias evaluation.pdf:application/pdf}
}


@article{Ng.2020,
 abstract = {Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve OOD generalization. However, in natural language, it is difficult to generate new examples that stay on the underlying data manifold. We introduce SSMBA, a data augmentation method for generating synthetic training examples by using a pair of corruption and reconstruction functions to move randomly on a data manifold. We investigate the use of SSMBA in the natural language domain, leveraging the manifold assumption to reconstruct corrupted text with masked language models. In experiments on robustness benchmarks across 3 tasks and 9 datasets, SSMBA consistently outperforms existing data augmentation methods and baseline models on both in-domain and OOD data, achieving gains of 0.8{\%} accuracy on OOD Amazon reviews, 1.8{\%} accuracy on OOD MNLI, and 1.4 BLEU on in-domain IWSLT14 German-English.},
 author = {Ng, Nathan and Cho, Kyunghyun and Ghassemi, Marzyeh},
 year = {2020},
 title = {{SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving  Out-of-Domain Robustness}},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning;Statistics - Machine Learning},
 pages = {1268--1283},
 journal = {{Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}},
 file = {Ng, Cho et al. 2020 - SSMBA Self-Supervised Manifold Based Data:Attachments/Ng, Cho et al. 2020 - SSMBA Self-Supervised Manifold Based Data.pdf:application/pdf}
}


@inproceedings{NilsEinecke.2022,
 abstract = {PDF | Modern communication has shifted strongly towards the digital domain. Technologies like chat applications and messengers provide means to connect... | Find, read and cite all the research you need on ResearchGate},
 author = {{Nils Einecke} and {Thomas H Weisswange}},
 title = {{Detecting Availability to Facilitate Social Communication}},
 url = {https://www.researchgate.net/profile/thomas-weisswange/publication/371957202_detecting_availability_to_facilitate_social_communication},
 year = {2022},
 file = {Nils Einecke, Thomas H Weisswange 2022 - Detecting Availability to Facilitate Social:Attachments/Nils Einecke, Thomas H Weisswange 2022 - Detecting Availability to Facilitate Social.pdf:application/pdf}
}


@inproceedings{Ocampo.2023,
 abstract = {Research on abusive content detection on social media has primarily focused on explicit forms of hate speech (HS), that are often identifiable by recognizing hateful words and expressions. Messages containing linguistically subtle and implicit forms of hate speech still constitute an open challenge for automatic hate speech detection. In this paper, we propose a new framework for generating adversarial implicit HS short-text messages using Auto-regressive Language Models. Moreover, we propose a strategy to group the generated implicit messages in complexity levels (EASY, MEDIUM, and HARD categories) characterizing how challenging these messages are for supervised classifiers. Finally, relying on (Dinan et al., 2019; Vidgen et al., 2021), we propose a ``build it, break it, fix it'', training scheme using HARD messages showing how iteratively retraining on HARD messages substantially leverages SOTA models' performances on implicit HS benchmarks.},
 author = {Ocampo, Nicolas and Cabrio, Elena and Villata, Serena},
 title = {{Playing the Part of the Sharp Bully: Generating Adversarial Examples for Implicit Hate Speech Detection}},
 url = {https://aclanthology.org/2023.findings-acl.173},
 pages = {2758--2772},
 publisher = {{Association for Computational Linguistics}},
 booktitle = {{Findings of the Association for Computational Linguistics: ACL 2023}},
 year = {2023},
 address = {Toronto, Canada},
 file = {sharp bully implicit hate speech:Attachments/sharp bully implicit hate speech.pdf:application/pdf}
}


@inproceedings{Okpala.2022,
 abstract = {Hate speech datasets contain bias which machine learning models propagate. When these models classify tweets written in African American English (AAE), they predict AAE tweets as hate/abusive at a higher rate than tweets written in Standard American English (SAE). This paper assesses bias in language models fine-tuned for hate speech detection and the effectiveness of adversarial learning in reducing such bias. We introduce AAEBERT, a pre-trained language model for African American English obtained by re-training BERT-base on AAE tweets. AAEBERT is used to extract the representation of each tweet in the various hate speech datasets and to classify tweets into two classes - AAE dialect and non-AAE dialect. A three-layer feedforward neural network that takes the representation from AAEBERT and a dialect label as input is used as the adversarial network for debiasing. We evaluate bias in language models finetuned for hate speech detection. Then assess the effectiveness of adversarial debiasing in these models by comparing results before and after adversarial debiasing is applied. Analysis reveals that the fine-tuned models are biased towards AAE, and adversarial debiasing is effective in reducing bias.},
 author = {Okpala, Ebuka and Cheng, Long and Mbwambo, Nicodemus and Luo, Feng},
 title = {{AAEBERT: Debiasing BERT-based Hate Speech Detection Models via Adversarial Learning}},
 publisher = {IEEE},
 booktitle = {{2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)}},
 year = {2022},
 doi = {10.1109/icmla55696.2022.00053},
 file = {aaebert adversial learning:Attachments/aaebert adversial learning.pdf:application/pdf}
}


@article{Olteanu.2019,
 abstract = {Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding {\textquotedbl}what the world thinks{\textquotedbl} about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the na{\"i}ve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. {\textquotedbl}For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated.{\textquotedbl} -Ursula Franklin.},
 author = {Olteanu, Alexandra and Castillo, Carlos and Diaz, Fernando and K{\i}c{\i}man, Emre},
 year = {2019},
 title = {{Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries}},
 url = {https://www.frontiersin.org/articles/10.3389/fdata.2019.00013/full},
 keywords = {biases;ethics;Evaluation;Social Media;User data},
 pages = {13},
 volume = {2},
 issn = {2624-909X},
 journal = {{Frontiers in Big Data}},
 doi = {10.3389/fdata.2019.00013},
 file = {Olteanu, Castillo et al. 2019 - Social Data:Attachments/Olteanu, Castillo et al. 2019 - Social Data.pdf:application/pdf}
}


@misc{OpenAI.2023,
 abstract = {We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10{\%} of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.},
 author = {OpenAI and Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and Anadkat, Shyamal and Avila, Red and Babuschkin, Igor and Balaji, Suchir and Balcom, Valerie and Baltescu, Paul and Bao, Haiming and Bavarian, Mohammad and Belgum, Jeff and Bello, Irwan and Berdine, Jake and Bernadett-Shapiro, Gabriel and Berner, Christopher and Bogdonoff, Lenny and Boiko, Oleg and Boyd, Madelaine and Brakman, Anna-Luisa and Brockman, Greg and Brooks, Tim and Brundage, Miles and Button, Kevin and Cai, Trevor and Campbell, Rosie and Cann, Andrew and Carey, Brittany and Carlson, Chelsea and Carmichael, Rory and Chan, Brooke and Chang, Che and Chantzis, Fotis and Chen, Derek and Chen, Sully and Chen, Ruby and Chen, Jason and Chen, Mark and Chess, Ben and Cho, Chester and Chu, Casey and Chung, Hyung Won and Cummings, Dave and Currier, Jeremiah and Dai, Yunxing and Decareaux, Cory and Degry, Thomas and Deutsch, Noah and Deville, Damien and Dhar, Arka and Dohan, David and Dowling, Steve and Dunning, Sheila and Ecoffet, Adrien and Eleti, Atty and Eloundou, Tyna and Farhi, David and Fedus, Liam and Felix, Niko and Fishman, Sim{\'o}n Posada and Forte, Juston and Fulford, Isabella and Gao, Leo and Georges, Elie and Gibson, Christian and Goel, Vik and Gogineni, Tarun and Goh, Gabriel and Gontijo-Lopes, Rapha and Gordon, Jonathan and Grafstein, Morgan and Gray, Scott and Greene, Ryan and Gross, Joshua and Gu, Shixiang Shane and Guo, Yufei and Hallacy, Chris and Han, Jesse and Harris, Jeff and He, Yuchen and Heaton, Mike and Heidecke, Johannes and Hesse, Chris and Hickey, Alan and Hickey, Wade and Hoeschele, Peter and Houghton, Brandon and Hsu, Kenny and Hu, Shengli and Hu, Xin and Huizinga, Joost and Jain, Shantanu and Jain, Shawn and Jang, Joanne and Jiang, Angela and Jiang, Roger and Jin, Haozhun and Jin, Denny and Jomoto, Shino and Jonn, Billie and Jun, Heewoo and Kaftan, Tomer and Kaiser, {\L}ukasz and Kamali, Ali and Kanitscheider, Ingmar and Keskar, Nitish Shirish and Khan, Tabarak and Kilpatrick, Logan and Kim, Jong Wook and Kim, Christina and Kim, Yongjik and Kirchner, Jan Hendrik and Kiros, Jamie and Knight, Matt and Kokotajlo, Daniel and Kondraciuk, {\L}ukasz and Kondrich, Andrew and Konstantinidis, Aris and Kosic, Kyle and Krueger, Gretchen and Kuo, Vishal and Lampe, Michael and Lan, Ikai and Lee, Teddy and Leike, Jan and Leung, Jade and Levy, Daniel and Li, Chak Ming and Lim, Rachel and Lin, Molly and Lin, Stephanie and Litwin, Mateusz and Lopez, Theresa and Lowe, Ryan and Lue, Patricia and Makanju, Anna and Malfacini, Kim and Manning, Sam and Markov, Todor and Markovski, Yaniv and Martin, Bianca and Mayer, Katie and Mayne, Andrew and McGrew, Bob and McKinney, Scott Mayer and McLeavey, Christine and McMillan, Paul and McNeil, Jake and Medina, David and Mehta, Aalok and Menick, Jacob and Metz, Luke and Mishchenko, Andrey and Mishkin, Pamela and Monaco, Vinnie and Morikawa, Evan and Mossing, Daniel and Mu, Tong and Murati, Mira and Murk, Oleg and M{\'e}ly, David and Nair, Ashvin and Nakano, Reiichiro and Nayak, Rajeev and Neelakantan, Arvind and Ngo, Richard and Noh, Hyeonwoo and Ouyang, Long and O'Keefe, Cullen and Pachocki, Jakub and Paino, Alex and Palermo, Joe and Pantuliano, Ashley and Parascandolo, Giambattista and Parish, Joel and Parparita, Emy and Passos, Alex and Pavlov, Mikhail and Peng, Andrew and Perelman, Adam and {Peres, Filipe de Avila Belbute} and Petrov, Michael and {Pinto, Henrique Ponde de Oliveira} and Michael and Pokorny and Pokrass, Michelle and Pong, Vitchyr H. and Powell, Tolly and Power, Alethea and Power, Boris and Proehl, Elizabeth and Puri, Raul and Radford, Alec and Rae, Jack and Ramesh, Aditya and Raymond, Cameron and Real, Francis and Rimbach, Kendra and Ross, Carl and Rotsted, Bob and Roussez, Henri and Ryder, Nick and Saltarelli, Mario and Sanders, Ted and Santurkar, Shibani and Sastry, Girish and Schmidt, Heather and Schnurr, David and Schulman, John and Selsam, Daniel and Sheppard, Kyla and Sherbakov, Toki and Shieh, Jessica and Shoker, Sarah and Shyam, Pranav and Sidor, Szymon and Sigler, Eric and Simens, Maddie and Sitkin, Jordan and Slama, Katarina and Sohl, Ian and Sokolowsky, Benjamin and Song, Yang and Staudacher, Natalie and Such, Felipe Petroski and Summers, Natalie and Sutskever, Ilya and Tang, Jie and Tezak, Nikolas and Thompson, Madeleine B. and Tillet, Phil and Tootoonchian, Amin and Tseng, Elizabeth and Tuggle, Preston and Turley, Nick and Tworek, Jerry and Uribe, Juan Felipe Cer{\'o}n and Vallone, Andrea and Vijayvergiya, Arun and Voss, Chelsea and Wainwright, Carroll and Wang, Justin Jay and Wang, Alvin and Wang, Ben and Ward, Jonathan and Wei, Jason and Weinmann, C. J. and Welihinda, Akila and Welinder, Peter and Weng, Jiayi and Weng, Lilian and Wiethoff, Matt and Willner, Dave and Winter, Clemens and Wolrich, Samuel and Wong, Hannah and Workman, Lauren and Wu, Sherwin and Wu, Jeff and Wu, Michael and Xiao, Kai and Xu, Tao and Yoo, Sarah and Yu, Kevin and Yuan, Qiming and Zaremba, Wojciech and Zellers, Rowan and Zhang, Chong and Zhang, Marvin and Zhao, Shengjia and Zheng, Tianhao and Zhuang, Juntang and Zhuk, William and Zoph, Barret},
 date = {15.03.2023},
 title = {{GPT-4 Technical Report}},
 url = {http://arxiv.org/pdf/2303.08774},
 keywords = {Artificial Intelligence (cs.AI);Computation and Language (cs.CL);Computer Science - Artificial Intelligence;Computer Science - Computation and Language},
 file = {OpenAI, Achiam et al. 15.03.2023 - GPT-4 Technical Report:Attachments/OpenAI, Achiam et al. 15.03.2023 - GPT-4 Technical Report.pdf:application/pdf}
}


@book{Park.2018,
 abstract = {Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, {\textquotedbl}You are a good woman{\textquotedbl} was considered {\textquotedbl}sexist{\textquotedbl} when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure gender biases on models trained with different abusive language datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three bias mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce gender bias by 90-98{\%} and can be extended to correct model bias in other scenarios.},
 author = {Park, Ji Ho and Shin, Jamin and Fung, Pascale},
 year = {2018},
publisher = {Association for Computational Linguistics},
 title = {{Reducing Gender Bias in Abusive Language Detection}},
 keywords = {Computer Science - Computation and Language},
 file = {Park, Shin et al. 2018 - Reducing Gender Bias in Abusive:Attachments/Park, Shin et al. 2018 - Reducing Gender Bias in Abusive.pdf:application/pdf}
}


@article{Patil.2022,
 author = {Patil, Pranita and Purcell, Kevin},
 year = {2022},
 title = {{Decorrelation-Based Deep Learning for Bias Mitigation}},
 pages = {110},
 volume = {14},
 number = {4},
 journal = {{Future Internet}},
 doi = {10.3390/fi14040110}
}


@misc{Pleiss.2017,
 abstract = {The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on what it means for a classification procedure to be {\textquotedbl}fair.{\textquotedbl} In this paper, we investigate the tension between minimizing error disparity across different population groups while maintaining calibrated probability estimates. We show that calibration is compatible only with a single error constraint (i.e. equal false-negatives rates across groups), and show that any algorithm that satisfies this relaxation is no better than randomizing a percentage of predictions for an existing classifier. These unsettling findings, which extend and generalize existing results, are empirically confirmed on several datasets.

First two authors contributed equally. NIPS 2017},
 author = {Pleiss, Geoff and Raghavan, Manish and Wu, Felix and Kleinberg, Jon and Weinberger, Kilian Q.},
 date = {2017},
 year = {2017},
 title = {{On Fairness and Calibration}},
 keywords = {Computers and Society (cs.CY);FOS: Computer and information sciences;Machine Learning (cs.LG);Machine Learning (stat.ML)},
 publisher = {arXiv},
 doi = {10.48550/arXiv.1709.02012},
 file = {On{\_}Fairness{\_}and{\_}Calibration:Attachments/On{\_}Fairness{\_}and{\_}Calibration.pdf:application/pdf}
}


@article{Powers.2011,
 abstract = {Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.},
 author = {Powers, David M. W.},
 year = {2011},
 title = {{Evaluation: from precision, recall and F-measure to ROC, informedness,  markedness and correlation}},
 keywords = {Computer Science - Learning;Statistics - Machine Learning;Statistics - Methodology},
 journal = {{International Journal of Machine Learning Technology 2:1 (2011)}},
 file = {Powers 2011 - Evaluation:Attachments/Powers 2011 - Evaluation.pdf:application/pdf}
}


@misc{Pradyumna.2021,
 author = {Pradyumna, Sahoo},
 editor = {{Analytics Drift}},
 year = {2021},
 title = {{Dealing With Racially-Biased Hate-Speech Detection Models}},
 urldate = {26.01.2023},
 file = {Pradyumna 2021 - Dealing With Racially-Biased Hate-Speech Detection:Attachments/Pradyumna 2021 - Dealing With Racially-Biased Hate-Speech Detection.pdf:application/pdf}
}


@article{Prakhar.2023,
 abstract = {In decision support system biases plays a vital role to lead unfair and discriminatory outcomes within the system, which can have serious consequences for people and society as a whole. While significant work has been done in classical machine learning and deep learning to address these difficulties, still there is a need for extensive surveys that evaluate various real-world applications and causes of bias in decision support systems. In this paper, we present a comprehensive survey that explores the biases, detection of biases, mitigation of biases, and fairness metrics to measure the degree of fairness in decision support systems. Further, we also identified several challenges related to biases such as minimizing biases when working with inadequate datasets, ensuring proper representation of protected attributes, developing efficient and direct methods for bias detection, identifying effective approaches for mitigating biases at various stages of the model, developing strategies to effectively mitigate multiple biases in the system to build a fair prediction model and at last, exploring and refining fairness metrics to achieve more fair results. We have also provided the research questions based on these challenges with the solutions and interesting future research avenues that might help to alleviate the problem of bias in decision support systems. We hope that this poll will motivate scholars to confront these issues and help the creation of more equitable systems.},
 author = {Prakhar, Jyoti and {Haider , Md. Tanwir Uddin}},
 year = {2023},
 title = {{Bias Detection and Mitigation within Decision Support System: A Comprehensive Survey}},
 url = {https://www.ijisae.org/index.php/IJISAE/article/view/3162},
 pages = {219--237},
 volume = {11},
 number = {3},
 journal = {{International Journal of Intelligent Systems and Applications in Engineering}}
}


@book{Qian.2019,
 abstract = {Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.},
 author = {Qian, Jing and Bethke, Anna and Liu, Yinyin and Belding, Elizabeth and Wang, William Yang},
 year = {2019},
 title = {{A Benchmark Dataset for Learning to Intervene in Online Hate Speech}},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computation and Language;Computer Science - Computers and Society},
 file = {Qian, Bethke et al. 2019 - A Benchmark Dataset for Learning:Attachments/Qian, Bethke et al. 2019 - A Benchmark Dataset for Learning.pdf:application/pdf}
}


@inproceedings{Qureshi.2023,
 abstract = {The prevalence of hate speech and offensive language on social media platforms such as Twitter has significant consequences, ranging from psychological harm to the polarization of societies. Consequently, social media companies have implemented content moderation...},
 author = {Qureshi, Muhammad Deedahwar Mazhar and Qureshi, M. Atif and Rashwan, Wael},
 title = {{Toward Inclusive Online Environments: Counterfactual-Inspired XAI for Detecting and Interpreting Hateful and Offensive Tweets}},
 url = {https://link.springer.com/chapter/10.1007/978-3-031-44070-0_5},
 pages = {97--119},
 publisher = {{Springer Nature Switzerland} and {Imprint Springer}},
 isbn = {978-3-031-44070-0},
 series = {{Communications in Computer and Information Science}},
 editor = {Longo, Luca},
 booktitle = {{Explainable Artificial Intelligence}},
 year = {2023},
 address = {Cham},
 doi = {10.1007/978-3-031-44070-0{\textunderscore }5},
 file = {Qureshi, Qureshi et al. 2023 - Toward Inclusive Online Environments:Attachments/Qureshi, Qureshi et al. 2023 - Toward Inclusive Online Environments.pdf:application/pdf}
}


@book{Radford.2018,
 author = {{Alec Radford} and {Karthik Narasimhan} and {Tim Salimans} and {Ilya Sutskever}},
 year = {2018},
 title = {{Improving Language Understanding by Generative Pre-Training}},
 url = {http://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf},
 publisher = {OpenAI},
 file = {Improving Language Understanding by Generative (3):Attachments/Improving Language Understanding by Generative (3).pdf:application/pdf}
}


@book{Radford.2019,
 author = {Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
 year = {2019},
 title = {{Language Models are Unsupervised Multitask Learners}},
 publisher = {OpenAI},
 file = {Language Models are Unsupervised Multitask (3):Attachments/Language Models are Unsupervised Multitask (3).pdf:application/pdf}
}


@incollection{Ramponi.2022,
 abstract = {Avoiding to rely on dataset artifacts to predict hate speech is at the cornerstone of robust and fair hate speech detection. In this paper we critically analyze lexical biases in hate speech detection via a cross-platform study, disentangling various types of spurious and authentic artifacts and analyzing their impact on out-of-distribution fairness and robustness. We experiment with existing approaches and propose simple yet surprisingly effective datacentric baselines. Our results on English data across four platforms show that distinct spurious artifacts require different treatments to ultimately attain both robustness and fairness in hate speech detection. To encourage research in this direction, we release all baseline models and the code to compute artifacts, pointing it out as a complementary and necessary addition to the data statements practice.},
 author = {Ramponi, Alan and Tonelli, Sara},
 title = {{Features or Spurious Artifacts? Data-centric Baselines for Fair and Robust Hate Speech Detection}},
 url = {https://cris.fbk.eu/handle/11582/333188},
 pages = {3027--3040},
 publisher = {{Association for Computational Linguistics}},
 booktitle = {{Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}},
 year = {2022},
 file = {Ramponi, Tonelli 2022 - Features or Spurious Artifacts:Attachments/Ramponi, Tonelli 2022 - Features or Spurious Artifacts.pdf:application/pdf}
}


@misc{Ravfogel.2020,
 abstract = {The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We present Iterative Null-space Projection (INLP), a novel method for removing information from neural representations. Our method is based on repeated training of linear classifiers that predict a certain property we aim to remove, followed by projection of the representations on their null-space. By doing so, the classifiers become oblivious to that target property, making it hard to linearly separate the data according to it. While applicable for multiple uses, we evaluate our method on bias and fairness use-cases, and show that our method is able to mitigate bias in word embeddings, as well as to increase fairness in a setting of multi-class classification.

Accepted as a long paper in ACL 2020},
 author = {Ravfogel, Shauli and Elazar, Yanai and Gonen, Hila and Twiton, Michael and Goldberg, Yoav},
 date = {2020},
year = {2020},
 title = {{Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection}},
 keywords = {Computation and Language (cs.CL);FOS: Computer and information sciences;Machine Learning (cs.LG)},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2004.07667},
 file = {Ravfogel, Elazar et al 2020 - Null It Out:Attachments/Ravfogel, Elazar et al 2020 - Null It Out.pdf:application/pdf}
}


@article{Ray.2023,
 author = {Ray, Partha Pratim},
 year = {2023},
 title = {{ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope}},
 pages = {121--154},
 volume = {3},
 issn = {2667-3452},
 journal = {{Internet of Things and Cyber-Physical Systems}},
 doi = {10.1016/j.iotcps.2023.04.003}
}


@misc{Raza.14.07.2023,
 abstract = {Discriminatory language and biases are often present in hate speech during conversations, which usually lead to negative impacts on targeted groups such as those based on race, gender, and religion. To tackle this issue, we propose an approach that involves a two-step process: first, detecting hate speech using a classifier, and then utilizing a debiasing component that generates less biased or unbiased alternatives through prompts. We evaluated our approach on a benchmark dataset and observed reduction in negativity due to hate speech comments. The proposed method contributes to the ongoing efforts to reduce biases in online discourse and promote a more inclusive and fair environment for communication.},
 author = {Raza, Shaina and Ding, Chen and Pandya, Deval},
 date = {14.07.2023},
 title = {{Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser  with Prompts}},
 url = {http://arxiv.org/pdf/2307.10213v1},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computation and Language}
}


@misc{Raza.2023,
 abstract = {Bias in textual data can lead to skewed interpretations and outcomes when the data is used. These biases could perpetuate stereotypes, discrimination, or other forms of unfair treatment. An algorithm trained on biased data ends up making decisions that disproportionately impact a certain group of people. Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data. To this end, we develop a comprehensive and robust framework \textsc{Nbias} that consists of a data layer, corpus contruction, model development layer and an evaluation layer. The dataset is constructed by collecting diverse data from various fields, including social media, healthcare, and job hiring portals. As such, we applied a transformer-based token classification model that is able to identify bias words/ phrases through a unique named entity. In the assessment procedure, we incorporate a blend of quantitative and qualitative evaluations to gauge the effectiveness of our models. We achieve accuracy improvements ranging from 1{\%} to 8{\%} compared to baselines. We are also able to generate a robust understanding of the model functioning, capturing not only numerical data but also the quality and intricacies of its performance. The proposed approach is applicable to a variety of biases and contributes to the fair and ethical use of textual data.},
 author = {Raza, Shaina and Garg, Muskan and Reji, Deepak John and Bashir, Syed Raza and Ding, Chen},
 date = {2023},
 title = {{NBIAS: A Natural Language Processing Framework for Bias Identification  in Text}},
 url = {http://arxiv.org/pdf/2308.01681v2},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computation and Language},
 file = {nbias nlp framework:Attachments/nbias nlp framework.pdf:application/pdf}
}


@article{Reinecke.2018,
 abstract = {Smartphones and other mobile devices have fundamentally changed patterns of Internet use in everyday life by making online access constantly available. The present paper offers a theoretical explication and empirical assessment of the concept of online vigilance, referring to users' permanent cognitive orientation towards online content and communication as well as their disposition to exploit these options constantly. Based on four studies, a validated and reliable self-report measure of online vigilance was developed. In combination, the results suggest that the Online Vigilance Scale (OVS) shows a stable factor structure in various contexts and user populations and provides future work in communication, psychology, and other social sciences with a new measure of the individual cognitive orientation towards ubiquitous online communication.},
 author = {Reinecke, Leonard and Klimmt, Christoph and Meier, Adrian and Reich, Sabine and Hefner, Doroth{\'e}e and Knop-Huelss, Katharina and Rieger, Diana and Vorderer, Peter},
 year = {2018},
 title = {{Permanently online and permanently connected: Development and validation of the Online Vigilance Scale}},
 url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205384},
 keywords = {Adolescent;Adult;Aged;Aged, 80 and over;Behavior;Communication;Factor Analysis, Statistical;Female;Humans;Internet;Male;Middle Aged;Models, Theoretical;Reproducibility of Results;Self Report;Smartphone;Text Messaging;Young Adult},
 pages = {e0205384},
 volume = {13},
 number = {10},
 issn = {1932-6203},
 journal = {{PLOS ONE}},
 doi = {10.1371/journal.pone.0205384},
 file = {Reinecke, Klimmt et al. 2018 - Permanently online and permanently connected:Attachments/Reinecke, Klimmt et al. 2018 - Permanently online and permanently connected.pdf:application/pdf}
}


@article{Ren.2018,
 abstract = {Learning to Reweight Examples for Robust Deep LearningMengye Ren,~Wenyuan Zeng,~Bin Yang,~Raquel UrtasunDeep neural networks have been shown t...},
 author = {{Mengye Ren} and {Wenyuan Zeng} and {Bin Yang} and {Raquel Urtasun}},
 year = {2018},
 title = {{Learning to Reweight Examples for Robust Deep Learning}},
 url = {http://proceedings.mlr.press/v80/ren18a.html},
 pages = {4334--4343},
 issn = {2640-3498},
 journal = {{International Conference on Machine Learning}},
 file = {Mengye Ren, Wenyuan Zeng et al. 2018 - Learning to Reweight Examples:Attachments/Mengye Ren, Wenyuan Zeng et al. 2018 - Learning to Reweight Examples.pdf:application/pdf}
}


@misc{Rodis.2023,
 abstract = {The current study focuses on systematically analyzing the recent advances in the field of Multimodal eXplainable Artificial Intelligence (MXAI). In particular, the relevant primary prediction tasks and publicly available datasets are initially described. Subsequently, a structured presentation of the MXAI methods of the literature is provided, taking into account the following criteria: a) The number of the involved modalities, b) The stage at which explanations are produced, and c) The type of the adopted methodology (i.e. mathematical formalism). Then, the metrics used for MXAI evaluation are discussed. Finally, a comprehensive analysis of current challenges and future research directions is provided.

26 pages, 11 figures},
 author = {Rodis, Nikolaos and Sardianos, Christos and Papadopoulos, Georgios Th. and Radoglou-Grammatikis, Panagiotis and Sarigiannidis, Panagiotis and Varlamis, Iraklis},
 date = {2023},
 title = {{Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions}},
 keywords = {Artificial Intelligence (cs.AI);FOS: Computer and information sciences},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2306.05731}
}


@book{RodriguezGonzalez.2023,
 year = {2023},
 title = {{Pattern Recognition}},
 address = {Cham},
 publisher = {{Springer Nature Switzerland}},
 isbn = {978-3-031-33782-6},
 series = {{Lecture Notes in Computer Science}},
 editor = {Rodr{\'i}guez-Gonz{\'a}lez, Ansel Yoan and P{\'e}rez-Espinosa, Humberto and Mart{\'i}nez-Trinidad, Jos{\'e} Francisco and Carrasco-Ochoa, Jes{\'u}s Ariel and Olvera-L{\'o}pez, Jos{\'e} Arturo},
 doi = {10.1007/978-3-031-33783-3}
}


@inproceedings{Roli.2001,
 abstract = {In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called ``overproduce and choose'' paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.},
 author = {Roli, Fabio and Giacinto, Giorgio and Vernazza, Gianni},
 title = {{Methods for Designing Multiple Classifier Systems}},
 url = {https://link.springer.com/chapter/10.1007/3-540-48219-9_8},
 pages = {78--87},
 publisher = {Springer},
 isbn = {978-3-540-48219-2},
 series = {{Lecture Notes in Computer Science}},
 editor = {Kittler, Josef},
 booktitle = {{Multiple classifier systems}},
 year = {2001},
 address = {Berlin},
 doi = {10.1007/3-540-48219-9{\textunderscore }8}
}


@article{Sachdeva.2022,
 author = {Sachdeva, Pratik S. and Barreto, Renata and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia and Kennedy, Chris J.},
 year = {2022},
 title = {{The measuring hate speech corpus: Leveraging Rasch Measurement Theory for Data Perspectivism}},
 url = {https://aclanthology.org/2022.nlperspectives-1.11/},
 pages = {83--94},
 journal = {{Proceedings of the 1st Workshop on Perspectivist Approaches to NLP}}
}


@inproceedings{Saha.2023,
 author = {Saha, Punyajoy and Das, Mithun and Mathew, Binny and Mukherjee, Animesh},
 title = {{Hate Speech: Detection, Mitigation and Beyond}},
 pages = {1232--1235},
 publisher = {ACM},
 isbn = {9781450394079},
 editor = {Chua, Tat-Seng and Lauw, Hady and Si, Luo and Terzi, Evimaria and Tsaparas, Panayiotis},
 booktitle = {{Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining}},
 year = {2023},
 address = {New York, NY, USA},
 doi = {10.1145/3539597.3572721}
}


@book{Sandeen.2021,
 abstract = {Cyberlaw will die and we will kill it /Ira Steven Nathenson -- Confidentiality creep and opportunistic privacy /David S. Levine --Disclosure /Sharon K. Sandeen --Exclusivity in data : how to best combine the patchwork of applicable European legal instruments /Herbert Zech --Data(base) rights? : misappropriation, property, and tales of trials and tribulations /Guido Westkamp --Big data in Japan : copyright, trade secret and new regime in 2018 /Tatsuhiro Ueno --Liability for the loss of data /Anette Gärtner --The emergence of a global standard for reasonable efforts? /Christoph Rademacher --Employers liability for trade secret infringement /Gintarė Surblytė-Namavičienė --The limits of trade secret protection in the EU /Tanya Aplin --Freedom of the media and trade secrets in Europe /Ulla-Maija Mylly --The DTSA [Defend Trade Secrets Act] and trade secret extraterritoriality /Elizabeth A. Rowe and Giulia C. Farrior --Jurisdiction and choice of law in trade secrets cases : the EU perspective /Ansgar Ohly --Data privacy in Europe and its reception under Japanese law /Henrike Weiden and Kensaku Takase --The right to explanation, explained /Margot E. Kaminski --The story of the California Consumer Privacy Act (CCPA), version 1.0 /Eric Goldman --Legislative developments on cybersecurity in the EU in the age of artificial intelligence /Faye Fangfei Wang.

{\textquotedbl}This fresh and insightful Research Handbook delivers global perspectives on information law and governance, delving into principles of information law in the areas of trade secrecy, privacy, data protection and cybersecurity.{\textquotedbl}},
 year = {2021},
 title = {{Research handbook on information law and governance}},
 address = {Cheltenham, UK and Northampton, Massachusetts},
 publisher = {{Edward Elgar Publishing Limited}},
 isbn = {9781788119924},
 series = {{Research handbooks in information law series}},
 editor = {Sandeen, Sharon K. and Rademacher, Christoph W. and Ohly, Ansgar}
}


@misc{Saravanakumar.2020,
 abstract = {With the increasing pervasive use of machine learning in social and economic settings, there has been an interest in the notion of machine bias in the AI community. Models trained on historic data reflect biases that exist in society and propagated them to the future through their decisions. There are three prominent metrics of machine fairness used in the community, and it has been shown statistically that it is impossible to satisfy them all at the same time. This has led to an ambiguity with regards to the definition of fairness. In this report, a causal perspective to the impossibility theorem of fairness is presented along with a causal goal for machine fairness.},
 author = {Saravanakumar, Kailash Karthik},
 date = {2020},
 title = {{The Impossibility Theorem of Machine Fairness -- A Causal Perspective}},
 keywords = {Artificial Intelligence (cs.AI);FOS: Computer and information sciences;Machine Learning (cs.LG);Machine Learning (stat.ML)},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2007.06024}
}


@inproceedings{Saufi.2023,
 abstract = {Artificial intelligence (AI) technology is becoming increasingly prevalent in society, offering a range of benefits and opportunities. However, with the rise of AI comes new challenges, particularly in hate speech. Hate speech, a type of expression that incites hatred or violence against individuals or groups based on ethnicity, religion, or other characteristics, has become a growing concern in Malaysia, with social media and online platforms becoming a breeding ground for such speech. In this context, AI technology has emerged as a potential solution for monitoring and regulating hate speech, but it also presents legal and ethical challenges that must be addressed. In view of double edge sword roles played by the development of AI, this article analyses the legal recourse available in Malaysia for disruptive AI technology and hate speech. The authors claim that AI systems are prone to errors and biases and that there is a risk of relying too much on such plans at the expense of human judgement. There are also concerns regarding the impact of AI on free expression and privacy rights. In addition, the author suggests that artificial intelligence be appropriately regulated to ensure that it is consistent with international human rights standards and national laws.},
 author = {Saufi, Nadia Nabila Mohd and Kamaruddin, Saslina and Mohammad, Ani Munirah and Jabar, Nurul Asyikeen Abd and Rosli, Wan Rosalili Wan and Talib, Zunirah Mohd},
 title = {{Disruptive AI Technology and Hate Speech: A Legal Redress in Malaysia}},
 publisher = {IEEE},
 booktitle = {{2023 International Conference on Disruptive Technologies (ICDT)}},
 year = {2023},
 doi = {10.1109/icdt57929.2023.10150942}
}


@misc{Scalena.2023,
 abstract = {Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences. Despite the effectiveness of such methods in improving the safety of model interactions, their impact on models' internal processes is still poorly understood. In this work, we apply popular detoxification approaches to several language models and quantify their impact on the resulting models' prompt dependence using feature attribution methods. We evaluate the effectiveness of counter-narrative fine-tuning and compare it with reinforcement learning-driven detoxification, observing differences in prompt reliance between the two methods despite their similar detoxification performances.},
 author = {Scalena, Daniel and Sarti, Gabriele and Nissim, Malvina and Fersini, Elisabetta},
 date = {2023},
 title = {{Let the Models Respond: Interpreting Language Model Detoxification  Through the Lens of Prompt Dependence}},
 url = {http://arxiv.org/pdf/2309.00751v1},
 keywords = {Computer Science - Computation and Language}
}


@article{Schilhab.2023,
 abstract = {Central and Eastern European Online Library - CEE journals, documents, articles, periodicals, books available online for download, Zeitschrfitendatenbank, Online Zeitschriften, Online Zeitschriftendatenbank},
 author = {Schilhab, Theresa and Esbensen, Gertrud and {Levrattom Valeria} and Suminas, Andrius},
 year = {2023},
 title = {{Phone Use, Offline Neglect, and Reachability: A Qualitative Study in Denmark, Lithuania, and Spain}},
 url = {https://www.ceeol.com/search/article-detail?id=1170691},
 keywords = {distractions;joint attention;occasional phubber;offline neglect;reachability;self-control;smart technology},
 pages = {118--147},
 volume = {13},
 number = {2},
 journal = {{Postmodernism Problems}},
 file = {hone Use, Offline Neglect 2023:Attachments/hone Use, Offline Neglect 2023.pdf:application/pdf}
}


@inproceedings{Schmidt.2017,
 author = {Schmidt, Anna and Wiegand, Michael},
 title = {{A Survey on Hate Speech Detection using Natural Language Processing}},
 pages = {1--10},
 publisher = {{Association for Computational Linguistics}},
 editor = {Ku, Lun-Wei and Li, Cheng-Te},
 booktitle = {{Proceedings of the Fifth International Workshop on Natural Language  Processing for Social Media}},
 year = {2017},
 address = {Stroudsburg, PA, USA},
 doi = {10.18653/v1/W17-1101}
}


@misc{Sheth.04.08.2023,
 abstract = {Social media platforms, despite their value in promoting open discourse, are often exploited to spread harmful content. Current deep learning and natural language processing models used for detecting this harmful content overly rely on domain-specific terms affecting their capabilities to adapt to generalizable hate speech detection. This is because they tend to focus too narrowly on particular linguistic signals or the use of certain categories of words. Another significant challenge arises when platforms lack high-quality annotated data for training, leading to a need for cross-platform models that can adapt to different distribution shifts. Our research introduces a cross-platform hate speech detection model capable of being trained on one platform's data and generalizing to multiple unseen platforms. To achieve good generalizability across platforms, one way is to disentangle the input representations into invariant and platform-dependent features. We also argue that learning causal relationships, which remain constant across diverse environments, can significantly aid in understanding invariant representations in hate speech. By disentangling input into platform-dependent features (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts. These features are then used to predict hate speech across unseen platforms. Our extensive experiments across four platforms highlight our model's enhanced efficacy compared to existing state-of-the-art methods in detecting generalized hate speech.},
 author = {Sheth, Paras and Kumarage, Tharindu and Moraffah, Raha and Chadha, Aman and Liu, Huan},
 year = {2023},
 date = {04.08.2023},
 title = {{Causality Guided Disentanglement for Cross-Platform Hate Speech  Detection}},
 url = {http://arxiv.org/pdf/2308.02080v2},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning}
}


@misc{Sheth.15.06.2023,
 abstract = {Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics. Due to the different policies of the platforms, different groups of people express hate in different ways. Furthermore, due to the lack of labeled data in some platforms it becomes challenging to build hate speech detection models. To this end, we revisit if we can learn a generalizable hate speech detection model for the cross platform setting, where we train the model on the data from one (source) platform and generalize the model across multiple (target) platforms. Existing generalization models rely on linguistic cues or auxiliary information, making them biased towards certain tags or certain kinds of words (e.g., abusive words) on the source platform and thus not applicable to the target platforms. Inspired by social and psychological theories, we endeavor to explore if there exist inherent causal cues that can be leveraged to learn generalizable representations for detecting hate speech across these distribution shifts. To this end, we propose a causality-guided framework, PEACE, that identifies and leverages two intrinsic causal cues omnipresent in hateful content: the overall sentiment and the aggression in the text. We conduct extensive experiments across multiple platforms (representing the distribution shift) showing if causal cues can help cross-platform generalization.},
 author = {Sheth, Paras and Kumarage, Tharindu and Moraffah, Raha and Chadha, Aman and Liu, Huan},
year = {2023},
 date = {15.06.2023},
 title = {{PEACE: Cross-Platform Hate Speech Detection- A Causality-guided  Framework}},
 url = {http://arxiv.org/pdf/2306.08804v1},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning}
}


@book{Sobhani.2023,
 abstract = {Natural language models and systems have been shown to reflect gender bias existing in training data. This bias can impact on the downstream task that machine learning models, built on this training data, are to accomplish. A variety of techniques have been proposed to mitigate gender bias in training data. 

In this paper we compare different gender bias mitigation approaches on a classifi- cation task. We consider mitigation techniques that manipulate the training data itself, including data scrubbing, gender-swapping, and counterfactual data augmentation approaches. We consider how gender neutralization , which replaces gender-specific terms with their gender-neutral equivalents, performs as a bias mitigation technique. We evaluate the effectiveness of the different approaches at reducing gender bias in the training data and consider the impact on task performance. 

We also look at the impact that de-biased word embeddings in the representation of the training data have on gender bias mitigation. Our results show that the performance of the classification task is not affected adversely by many of them but we show a significant variation in the effectiveness of the different gender bias mitigation techniques. 

These gender bias mitigation approaches typically focus on lexical or explicit gender in text and while these approaches reduce gender bias in training data, they do not remove it completely. We investigate the source of gender bias in the training data through the use of stereotypical masculine and feminine words that can signal gender implicitly due to gender roles or behaviour. We show how certain words which have an associated implicit gender may contribute to gender bias, even after attempts to neutralize gender in language.},
 author = {Sobhani, Nasim and Delany, Sarah Jane},
 year = {2023},
 title = {{A Comparative Study of Gender Bias Mitigation Techniques and the Role of Implicit Stereotypes}},
 doi = {10.21203/rs.3.rs-3126256/v1 },
 file = {comparative study of gender bias debiasing:Attachments/comparative study of gender bias debiasing.pdf:application/pdf}
}


@proceedings{Spinellis.2021,
 year = {2021},
 title = {{Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9781450385626 },
 editor = {Spinellis, Diomidis and Gousios, Georgios and Chechik, Marsha and {Di Penta}, Massimiliano},
 doi = {10.1145/3468264 }
}


@book{Stanisic.2023,
 abstract = {Hate speech is a problem which puts its targets at risk of serious harm. It spreads fast and has a real

influence on the society because of the ubiquity of the internet and social media, and so various research

efforts have been put to find solutions to automatic hate speech detection. Despite major developments

in the field, challenges with data scarcity and characteristics often cause solutions reported in previous

research to overfit the datasets that were used to train and test them, which results in dramatic performance

losses and failures in generalization. This study addressed this issue, it tried to find a solution that would

mitigate overfitting effects originating from these issues and enhance language-based classifier with extra

user information concerning one's social connections. It compared two single-source models -- one based on

textual information, and the other based on information concerning one's social connections and proposed

a joint decision engine that selects the model whose class assignment was more certain for a given instance.

Although the single-source models' performance dropped drastically on test data, the joint decision engine

succeeded in reducing some of the issues related to overfitting, improving the overall performance. This

observation suggests that simple solutions might be efficient in reducing model overfit and paves the way

towards validating these findings.},
 author = {Stani{\v{s}}i{\'c}, Milan},
 year = {2023},
 title = {{IDENTIFYING HATE SPEECH IN SOCIAL MEDIA THROUGH CONTENT AND SOCIAL CONNECTIONS ANALYSIS}},
 url = {https://gupea.ub.gu.se/handle/2077/77243},
 keywords = {classification;hate speech;natural language processing;Social Media;Text},
 file = {Stani{\v{s}}i{\'c} 2023 - IDENTIFYING HATE SPEECH IN SOCIAL:Attachments/Stani{\v{s}}i{\'c} 2023 - IDENTIFYING HATE SPEECH IN SOCIAL.pdf:application/pdf}
}


@misc{Steinborn.16.06.2023,
 abstract = {In efforts to keep up with the rapid progress and use of large language models, gender bias research is becoming more prevalent in NLP. Non-English bias research, however, is still in its infancy with most work focusing on English. In our work, we study how grammatical gender bias relating to politeness levels manifests in Japanese and Korean language models. Linguistic studies in these languages have identified a connection between gender bias and politeness levels, however it is not yet known if language models reproduce these biases. We analyze relative prediction probabilities of the male and female grammatical genders using templates and find that informal polite speech is most indicative of the female grammatical gender, while rude and formal speech is most indicative of the male grammatical gender. Further, we find politeness levels to be an attack vector for allocational gender bias in cyberbullying detection models. Cyberbullies can evade detection through simple techniques abusing politeness levels. We introduce an attack dataset to (i) identify representational gender bias across politeness levels, (ii) demonstrate how gender biases can be abused to bypass cyberbullying detection models and (iii) show that allocational biases can be mitigated via training on our proposed dataset. Through our findings we highlight the importance of bias research moving beyond its current English-centrism.},
 author = {Steinborn, Victor and Maronikolakis, Antonis and Sch{\"u}tze, Hinrich},
 date = {16.06.2023},
 title = {{Politeness Stereotypes and Attack Vectors: Gender Stereotypes in  Japanese and Korean Language Models}},
 url = {http://arxiv.org/pdf/2306.09752v1},
 keywords = {Computer Science - Artificial Intelligence;Computer Science - Computation and Language;Computer Science - Computers and Society;Computer Science - Learning}
}


@article{Stevenson.2017,
 author = {Stevenson, Megan T.},
 year = {2017},
 title = {{Assessing Risk Assessment in Action}},
 issn = {1556-5068},
 journal = {{SSRN Electronic Journal}},
 doi = {10.2139/ssrn.3016088},
 file = {Stevenson 2017 - Assessing Risk Assessment in Action:Attachments/Stevenson 2017 - Assessing Risk Assessment in Action.pdf:application/pdf}
}


@article{Suresh.2021,
 abstract = {As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable downstream consequences, it is critical that we understand when and how harm might be introduced throughout the ML life cycle. In this paper, we provide a framework that identifies seven distinct potential sources of downstream harm in machine learning, spanning data collection, development, and deployment. In doing so, we aim to facilitate more productive and precise communication around these issues, as well as more direct, application-grounded ways to mitigate them.},
 author = {Suresh, Harini and Guttag, John V.},
 year = {2021},
journal = {EAAMO '21: Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization},
 title = {{A Framework for Understanding Sources of Harm throughout the Machine  Learning Life Cycle}},
 url = {http://arxiv.org/pdf/1901.10002v5},
 keywords = {AI ethics;algorithmic bias;allocative harm;Computer Science - Learning;fairness in machine learning;representational harm;societal implications of machine learning;Statistics - Machine Learning},
publisher = {Association for Computing Machinery},
 pages = {1--9},
 volume = {32},
 doi = {10.1145/3465416.3483305},
 file = {a framework for understanding sources of harm throughout the machine learning life cycle:Attachments/a framework for understanding sources of harm throughout the machine learning life cycle.pdf:application/pdf}
}


@book{Swayamdipta.2020,
 abstract = {Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Maps---a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training (training dynamics) for building data maps. This yields two intuitive measures for each example---the model's confidence in the true class, and the variability of this confidence across epochs---obtained in a single run of training. Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics. First, our data maps show the presence of {\textquotedbl}ambiguous{\textquotedbl} regions with respect to the model, which contribute the most towards out-of-distribution generalization. Second, the most populous regions in the data are {\textquotedbl}easy to learn{\textquotedbl} for the model, and play an important role in model optimization. Finally, data maps uncover a region with instances that the model finds {\textquotedbl}hard to learn{\textquotedbl}; these often correspond to labeling errors. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.},
 author = {Swayamdipta, Swabha and Schwartz, Roy and Lourie, Nicholas and Wang, Yizhong and Hajishirzi, Hannaneh and Smith, Noah A. and Choi, Yejin},
 year = {2020},
 title = {{Dataset Cartography: Mapping and Diagnosing Datasets with Training  Dynamics}},
 keywords = {Computer Science - Computation and Language},
 file = {Swayamdipta, Schwartz et al. 2020 - Dataset Cartography:Attachments/Swayamdipta, Schwartz et al. 2020 - Dataset Cartography.pdf:application/pdf}
}


@proceedings{Syriani.2022,
 year = {2022},
 title = {{Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems}},
 address = {New York, NY, USA},
 publisher = {ACM},
 isbn = {9781450394666},
 editor = {Syriani, Eugene and Sahraoui, Houari},
 doi = {10.1145/3550355}
}


@misc{Tan.23.06.2019,
 abstract = {The ability to understand logical relationships between sentences is an important task in language understanding. To aid in progress for this task, researchers have collected datasets for machine learning and evaluation of current systems. However, like in the crowdsourced Visual Question Answering (VQA) task, some biases in the data inevitably occur. In our experiments, we find that performing classification on just the hypotheses on the SNLI dataset yields an accuracy of 64{\%}. We analyze the bias extent in the SNLI and the MultiNLI dataset, discuss its implication, and propose a simple method to reduce the biases in the datasets.},
 author = {Tan, Shawn and Shen, Yikang and Huang, Chin-wei and Courville, Aaron},
 date = {23.06.2019},
 title = {{Investigating Biases in Textual Entailment Datasets}},
 url = {http://arxiv.org/pdf/1906.09635v1},
 keywords = {Computer Science - Computation and Language;Computer Science - Learning},
 file = {Investigating Biases in Textual Entailment Datasets:Attachments/Investigating Biases in Textual Entailment Datasets.pdf:application/pdf}
}


@inproceedings{Themeli.2023,
 abstract = {The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Hate Speech content on microblogging platforms such as Twitter. Current EU and US legislation against hateful language, in conjunction with the large amount of data...},
 author = {Themeli, Chrysoula and Giannakopoulos, George and Pittaras, Nikiforos},
 title = {{A Study of Text Representations for Hate Speech Detection}},
 url = {https://link.springer.com/chapter/10.1007/978-3-031-24340-0_32} ,
 pages = {424--437},
 publisher = {{Springer, Cham}},
 year = {2023},
 doi = {10.1007/978-3-031-24340-0{\textunderscore }32},
 file = {Themeli, Giannakopoulos et al. 2023 - A Study of Text Representations:Attachments/Themeli, Giannakopoulos et al. 2023 - A Study of Text Representations.pdf:application/pdf}
}


@article{Tian.2022,
 author = {Tian, Yingjie and Zhang, Yuqi},
 year = {2022},
 title = {{A comprehensive survey on regularization strategies in machine learning}},
 pages = {146--166},
 volume = {80},
 issn = {15662535},
 journal = {{Information Fusion}},
 doi = {10.1016/j.inffus.2021.11.005}
}


@misc{Tokpo.2023,
 abstract = {To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this. In this work, we design a probe to investigate the effects that some of the major intrinsic gender bias mitigation strategies have on downstream text classification tasks. We discover that instead of resolving gender bias, intrinsic mitigation techniques and metrics are able to hide it in such a way that significant gender information is retained in the embeddings. Furthermore, we show that each mitigation technique is able to hide the bias from some of the intrinsic bias measures but not all, and each intrinsic bias measure can be fooled by some mitigation techniques, but not all. We confirm experimentally, that none of the intrinsic mitigation techniques used without any other fairness intervention is able to consistently impact extrinsic bias. We recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks.},
 author = {Tokpo, Ewoenam and Delobelle, Pieter and Berendt, Bettina and Calders, Toon},
 date = {2023},
 title = {{How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification}},
 keywords = {Computation and Language (cs.CL);FOS: Computer and information sciences},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2301.12855}
}


@article{Tosteson.2014,
 abstract = {IMPORTANCE

False-positive mammograms, a common occurrence in breast cancer screening programs, represent a potential screening harm that is currently being evaluated by the US Preventive Services Task Force.

OBJECTIVE

To measure the effect of false-positive mammograms on quality of life by measuring personal anxiety, health utility, and attitudes toward future screening.

DESIGN, SETTING, AND PARTICIPANTS

The Digital Mammographic Imaging Screening Trial (DMIST) quality-of-life substudy telephone survey was performed shortly after screening and 1 year later at 22 DMIST sites and included randomly selected DMIST participants with positive and negative mammograms.

EXPOSURE

Mammogram requiring follow-up testing or referral without a cancer diagnosis.

MAIN OUTCOMES AND MEASURES

The 6-question short form of the Spielberger State-Trait Anxiety Inventory state scale (STAI-6) and the EuroQol EQ-5D instrument with US scoring. Attitudes toward future screening as measured by women's self-report of future intention to undergo mammographic screening and willingness to travel and stay overnight to undergo a hypothetical new type of mammography that would identify as many cancers with half the false-positive results.

RESULTS

Among 1450 eligible women invited to participate, 1226 (84.6{\%}) were enrolled, with follow-up interviews obtained in 1028 (83.8{\%}). Anxiety was significantly higher for women with false-positive mammograms (STAI-6, 35.2 vs 32.7), but health utility scores did not differ and there were no significant differences between groups at 1 year. Future screening intentions differed by group (25.7{\%} vs 14.2{\%} more likely in false-positive vs negative groups); willingness to travel and stay overnight did not (9.9{\%} vs 10.5{\%} in false-positive vs negative groups). Future screening intention was significantly increased among women with false-positive mammograms (odds ratio, 2.12; 95{\%} CI, 1.54-2.93), younger age (2.78; 1.5-5.0), and poorer health (1.63; 1.09-2.43). Women's anticipated high-level anxiety regarding future false-positive mammograms was associated with willingness to travel overnight (odds ratio, 1.94; 95{\%} CI, 1.28-2.95).

CONCLUSIONS AND RELEVANCE

False-positive mammograms were associated with increased short-term anxiety but not long-term anxiety, and there was no measurable health utility decrement. False-positive mammograms increased women's intention to undergo future breast cancer screening and did not increase their stated willingness to travel to avoid a false-positive result. Our finding of time-limited harm after false-positive screening mammograms is relevant for clinicians who counsel women on mammographic screening and for screening guideline development groups.},
 author = {Tosteson, Anna N. A. and Fryback, Dennis G. and Hammond, Cristina S. and Hanna, Lucy G. and Grove, Margaret R. and Brown, Mary and Wang, Qianfei and Lindfors, Karen and Pisano, Etta D.},
 year = {2014},
 title = {{Consequences of false-positive screening mammograms}},
 keywords = {Aged;Anxiety;False Positive Reactions;Female;Follow-Up Studies;Humans;Mammography/adverse effects/psychology;Middle Aged;Quality of Life},
 pages = {954--961},
 volume = {174},
 number = {6},
 issn = {2168-6106},
 journal = {{JAMA Internal Medicine}},
 doi = {10.1001/jamainternmed.2014.981},
 file = {Tosteson, Fryback et al. 2014 - Consequences of false-positive screening mammograms:Attachments/Tosteson, Fryback et al. 2014 - Consequences of false-positive screening mammograms.pdf:application/pdf}
}


@article{Uyheng.2020,
 abstract = {Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.},
 author = {Uyheng, Joshua and Carley, Kathleen M.},
 year = {2020},
 title = {{Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines}},
 pages = {445--468},
 volume = {3},
 number = {2},
 journal = {{Journal of computational social science}},
 doi = {10.1007/s42001-020-00087-4}
}


@misc{vanderWal.2022,
 abstract = {Detecting and mitigating harmful biases in modern language models are widely recognized as crucial, open problems. In this paper, we take a step back and investigate how language models come to be biased in the first place. We use a relatively small language model, using the LSTM architecture trained on an English Wikipedia corpus. With full access to the data and to the model parameters as they change during every step while training, we can map in detail how the representation of gender develops, what patterns in the dataset drive this, and how the model's internal state relates to the bias in a downstream task (semantic textual similarity). We find that the representation of gender is dynamic and identify different phases during training. Furthermore, we show that gender information is represented increasingly locally in the input embeddings of the model and that, as a consequence, debiasing these can be effective in reducing the downstream bias. Monitoring the training dynamics, allows us to detect an asymmetry in how the female and male gender are represented in the input embeddings. This is important, as it may cause naive mitigation strategies to introduce new undesirable biases. We discuss the relevance of the findings for mitigation strategies more generally and the prospects of generalizing our methods to larger language models, the Transformer architecture, other languages and other undesirable biases.},
 author = {{van der Wal}, Oskar and Jumelet, Jaap and Schulz, Katrin and Zuidema, Willem},
 date = {2022},
year = {2022},
 title = {{The Birth of Bias: A case study on the evolution of gender bias in an English language model}},
 keywords = {Artificial Intelligence (cs.AI);Computation and Language (cs.CL);FOS: Computer and information sciences},
 publisher = {arXiv},
 doi = {10.48550/arXiv.2207.10245},
 file = {birth of bias:Attachments/birth of bias.pdf:application/pdf}
}


@article{VujicicStankovic.2023,
 abstract = {Hate Speech encompasses different forms of trolling, bullying, harassment, and threats directed against specific individuals or groups. This phenomena is mainly expressed on Social Networks. For sports players, Social Media is a means of communication with the widest part of their fans and a way to face different cyber-aggression forms. These virtual attacks can harm players, distress them, cause them to feel bad for a long time, or even escalate into physical violence. To date, athletes were not observed as a vulnerable group, so they were not a subject of automatic Hate Speech detection and recognition from content published on Social Media. This paper explores whether a model trained on the dataset from one Social Media and not related to any specific domain can be efficient for the Hate Speech binary classification of test sets regarding the sports domain. The experiments deal with Hate Speech detection in Serbian. BiLSTM deep neural network was learned with different parameters, and the results showed high Precision of detecting Hate Speech in sports domain (96{\%} and 97{\%}) and pretty low Recall.},
 author = {{Vuji{\v{c}}i{\'c} Stankovi{\'c}}, Sta{\v{s}}a and Mladenovi{\'c}, Miljana},
 year = {2023},
 title = {{An approach to automatic classification of hate speech in sports domain on social media}},
 volume = {10},
 number = {1},
 journal = {{Journal of Big Data}},
 doi = {10.1186/s40537-023-00766-9},
 file = {Vuji{\v{c}}i{\'c} Stankovi{\'c}, Mladenovi{\'c} 2023 - An approach to automatic classification:Attachments/Vuji{\v{c}}i{\'c} Stankovi{\'c}, Mladenovi{\'c} 2023 - An approach to automatic classification.pdf:application/pdf}
}


@article{Wachter.2021,
 abstract = {Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups. Recognising this problem, much work has emerged in recent years to test for bias in machine learning and AI systems using various fairness and bias

metrics. Often these metrics address technical bias but ignore the underlying causes of inequality. In this paper we make three contributions. First, we assess the compatibility of fairness metrics used in machine learning against the aims and purpose of EU non-discrimination law. We show that the fundamental aim of the law is not only to prevent ongoing discrimination, but also to change society, policies, and practices to `level the playing field' and achieve substantive rather than merely formal equality. Based on this, we then propose a novel classification scheme for fairness metrics in machine learning based on how they handle pre-existing bias and thus align with the aims of non-discrimination law. Specifically, we distinguish between `bias preserving' and `bias transforming' fairness metrics. Our classification system is intended to bridge the gap between non-discrimination law and decisions around how to measure fairness in machine learning and AI in practice. Finally, we show that the legal need for justification in cases of indirect discrimination can impose additional obligations on developers, deployers, and users that choose to use bias preserving fairness metrics when making decisions about individuals because they can give rise to prima facie discrimination. To achieve substantive

equality in practice, and thus meet the aims of the law, we instead recommend using bias transforming metrics. To conclude, we provide concrete recommendations including a user-friendly checklist for choosing the most appropriate fairness metric for uses of machine learning and AI under EU non-discrimination law.},
 author = {Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
 year = {2021},
 title = {{Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU Non-Discrimination Law}},
 issn = {1556-5068},
 journal = {{SSRN Electronic Journal}},
 doi = {10.2139/ssrn.3792772},
 file = {Wachter, Mittelstadt et al. 2021 - Bias Preservation in Machine Learning:Attachments/Wachter, Mittelstadt et al. 2021 - Bias Preservation in Machine Learning.pdf:application/pdf;the-legality-of-fairness-metrics-under-eu-non-discrimination-law:Attachments/the-legality-of-fairness-metrics-under-eu-non-discrimination-law.pdf:application/pdf}
}


@book{Wadsworth.2018,
 abstract = {Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and Florida, which past research has shown to be biased against black inmates according to certain measures of fairness. To counteract this racial bias, we present an adversarially-trained neural network that predicts recidivism and is trained to remove racial bias. When comparing the results of our model to COMPAS, we gain predictive accuracy and get closer to achieving two out of three measures of fairness: parity and equality of odds. Our model can be generalized to any prediction and demographic. This piece of research contributes an example of scientific replication and simplification in a high-stakes real-world application like recidivism prediction.},
 author = {Wadsworth, Christina and Vera, Francesca and Piech, Chris},
 year = {2018},
 title = {{Achieving Fairness through Adversarial Learning: an Application to  Recidivism Prediction}},
 keywords = {Computer Science - Learning;Statistics - Machine Learning},
 file = {Wadsworth, Vera et al. 2018 - Achieving Fairness through Adversarial Learning:Attachments/Wadsworth, Vera et al. 2018 - Achieving Fairness through Adversarial Learning.pdf:application/pdf}
}


@proceedings{Walker.2018,
 year = {2018},
 title = {{Proceedings of the 2018 Conference of the North American Chapter of}},
 address = {Stroudsburg, PA, USA},
 publisher = {{Association for Computational Linguistics}},
 editor = {Walker, Marilyn and Ji, Heng and Stent, Amanda},
 doi = {10.18653/v1/N18-2}
}


@article{Wich.2022,
 abstract = {Recently, numerous datasets have been produced as research activities in the field of automatic detection of abusive language or hate speech have increased. A problem with this diversity is that they often differ, among other things, in context, platform, sampling process, collection strategy, and labeling schema. There have been surveys on these datasets, but they compare the datasets only superficially. Therefore, we developed a bias and comparison framework for abusive language datasets for their in-depth analysis and to provide a comparison of five English and six Arabic datasets. We make this framework available to researchers and data scientists who work with such datasets to be aware of the properties of the datasets and consider them in their work.},
 author = {Wich, Maximilian and Eder, Tobias and {Al Kuwatly}, Hala and Groh, Georg},
 year = {2022},
 title = {{Bias and comparison framework for abusive language datasets}},
 keywords = {Abusive language detection;Arabic;Bias;English;Hate speech detection},
 pages = {79--101},
 volume = {2},
 number = {1},
 journal = {{AI and ethics}},
 doi = {10.1007/s43681-021-00081-0},
 file = {Bias{\_}and{\_}comparison{\_}framework{\_}for{\_}abusive{\_}language{\_}datasets-1:Attachments/Bias{\_}and{\_}comparison{\_}framework{\_}for{\_}abusive{\_}language{\_}datasets-1.pdf:application/pdf}
}


@inproceedings{Wiegand.2019,
 abstract = {We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.},
 author = {Wiegand, Michael and Ruppenhofer, Josef and Kleinbauer, Thomas},
 title = {{Detection of Abusive Language: the Problem of Biased Datasets}},
 pages = {602--608},
 publisher = {{Association for Computational Linguistics}},
 editor = {Burstein, Jill and Doran, Christy and Solorio, Thamar},
 booktitle = {{Proceedings of the 2019 Conference of the North}},
 year = {2019},
 address = {Stroudsburg, PA, USA},
 doi = {10.18653/v1/N19-1060}
}


@article{Wolfers.2022,
 abstract = {In this review, we systematize work on the relationship between social media use and stress by providing a functional perspective that distinguishes between three functions that social media can have in the stages of the stress-coping process: as stressors, as resources, and as coping tools. Current research provides evidence that social media can cause stress, serve as resources, and can be used as a tool for various coping strategies, but it remains unclear when social media can successfully mitigate stress. Future research should use more fine-grained research designs that consider the timing of social media use, the situational context, and the encountered content to determine when social media serves which function and when social media reduces or increases stress.},
 author = {Wolfers, Lara N. and Utz, Sonja},
 year = {2022},
 title = {{Social media use, stress, and coping}},
 url = {https://www.sciencedirect.com/science/article/pii/S2352250X22000070},
 keywords = {Adaptation, Psychological;Coping;Coping tool;Humans;Social Media;Stress;Stress, Psychological;Stressor},
 pages = {101305},
 volume = {45},
 issn = {2352-250X},
 journal = {{Current Opinion in Psychology}},
 doi = {10.1016/j.copsyc.2022.101305},
 file = {Wolfers, Utz 2022 - Social media use:Attachments/Wolfers, Utz 2022 - Social media use.pdf:application/pdf}
}


@inproceedings{Wu.2020,
 author = {Wu, Wenying and Protopapas, Pavlos and Yang, Zheng and Michalatos, Panagiotis},
 title = {{Gender Classification and Bias Mitigation in Facial Images}},
 publisher = {ACM},
 booktitle = {{12th ACM Conference on Web Science}},
 year = {2020},
 address = {New York, NY, USA},
 doi = {10.1145/3394231.3397900},
 file = {Wu, Protopapas et al. 2020 - Gender Classification and Bias Mitigation:Attachments/Wu, Protopapas et al. 2020 - Gender Classification and Bias Mitigation.pdf:application/pdf}
}


@inproceedings{Wulczyn.2017,
 author = {Wulczyn, Ellery and Thain, Nithum and Dixon, Lucas},
 title = {{Ex Machina: Personal Attacks Seen at Scale}},
 publisher = {{International World Wide Web Conferences Steering Committee}},
 booktitle = {{Proceedings of the 26th International Conference on World Wide Web}},
 year = {2017},
 address = {Republic and Canton of Geneva, Switzerland},
 doi = {10.1145/3038912.3052591}
}


@misc{Xia.2020,
 abstract = {In current hate speech datasets, there exists a high correlation between annotators' perceptions of toxicity and signals of African American English (AAE). This bias in annotated training data and the tendency of machine learning models to amplify it cause AAE text to often be mislabeled as abusive/offensive/hate speech with a high false positive rate by current hate speech classifiers. In this paper, we use adversarial training to mitigate this bias, introducing a hate speech classifier that learns to detect toxic sentences while demoting confounds corresponding to AAE texts. Experimental results on a hate speech dataset and an AAE dataset suggest that our method is able to substantially reduce the false positive rate for AAE text while only minimally affecting the performance of hate speech classification.},
 author = {Xia, Mengzhou and Field, Anjalie and Tsvetkov, Yulia},
 date = {2020},
 year = {2020},
 title = {{Demoting Racial Bias in Hate Speech Detection}},
 url = {http://arxiv.org/pdf/2005.12246v1},
 keywords = {Computer Science - Computation and Language},
 file = {Xia, Field et al 25052020 - Demoting Racial Bias in Hate:Attachments/Xia, Field et al 25052020 - Demoting Racial Bias in Hate.pdf:application/pdf}
}


@article{Yang.2020,
 abstract = {Recent advances in commonsense reasoning depend on large-scale human-annotated training sets to achieve peak performance. However, manual curation of training sets is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit to. We propose a novel generative data augmentation technique, G-DAUGˆC, that aims to achieve more accurate and robust learning in a low-resource setting. Our approach generates synthetic examples using pretrained language models and selects the most informative and diverse set of examples for data augmentation. On experiments with multiple commonsense reasoning benchmarks, G-DAUGˆC consistently outperforms existing data augmentation methods based on back-translation, establishing a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA, as well as enhances out-of-distribution generalization, proving to be robust against adversaries or perturbations. Our analysis demonstrates that G-DAUGˆC produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.},
 author = {Yang, Yiben and Malaviya, Chaitanya and Fernandez, Jared and Swayamdipta, Swabha and {Le Bras}, Ronan and Wang, Ji-Ping and Bhagavatula, Chandra and Choi, Yejin and Downey, Doug},
 year = {2020},
 title = {{Generative Data Augmentation for Commonsense Reasoning}},
 pages = {1008--1025},
 journal = {{Findings of the Association for Computational Linguistics: EMNLP 2020}},
 doi = {10.18653/v1/2020.findings-emnlp.90},
 file = {Yang, Malaviya et al. - Generative Data Augmentation for Commonsense:Attachments/Yang, Malaviya et al. - Generative Data Augmentation for Commonsense.pdf:application/pdf}
}


@misc{Yin.2021,
 abstract = {Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are, reason why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.},
 author = {Yin, Wenjie and Zubiaga, Arkaitz},
 date = {17.02.2021},
 year = {2021},
 title = {{Towards generalisable hate speech detection: a review on obstacles and  solutions}},
 url = {http://arxiv.org/pdf/2102.08886v1},
 keywords = {Computer Science - Computation and Language},
 file = {Towards generalisable hate speech detection a review on obstacles and solutions:Attachments/Towards generalisable hate speech detection a review on obstacles and solutions.pdf:application/pdf}
}


@inproceedings{Yohannis.2022,
 author = {Yohannis, Alfa and Kolovos, Dimitris},
 title = {{Towards model-based bias mitigation in machine learning}},
 pages = {143--153},
 publisher = {ACM},
 isbn = {9781450394666},
 editor = {Syriani, Eugene and Sahraoui, Houari},
 booktitle = {{Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems}},
 year = {2022},
 address = {New York, NY, USA},
 doi = {10.1145/3550355.3552401}
}


@article{Yong.2012,
 author = {Yong, Yang},
 year = {2012},
 title = {{The Research of Imbalanced Data Set of Sample Sampling Method Based on K-Means Cluster and Genetic Algorithm}},
 pages = {164--170},
 volume = {17},
 issn = {1876-6102},
 journal = {{Energy Procedia}},
 doi = {10.1016/j.egypro.2012.02.078}
}


@article{Zhang.2018,
 abstract = {As a promising area in machine learning, multi-task learning (MTL) aims to improve the performance of multiple related learning tasks by leveraging useful information among them. In this paper, we give an overview ofMTL by first giving a definition ofMTL. Then several different settings ofMTL are introduced, including multi-task supervised learning, multi-task unsupervised learning, multi-task semi-supervised learning, multi-task active learning, multi-task reinforcement learning, multi-task online learning and multi-taskmulti-view learning. For each setting, representative MTL models are presented. In order to speed up the learning process, parallel and distributed MTL models are introduced. Many areas, including computer vision, bioinformatics, health informatics, speech, natural language processing, web applications and ubiquitous computing, use MTL to improve the performance ofthe applications involved and some representative works are reviewed. Finally, recent theoretical analyses for MTL are presented.},
 author = {Zhang, Yu and Yang, Qiang},
 year = {2018},
 title = {{An overview of multi-task learning}},
 pages = {30--43},
 volume = {5},
 number = {1},
 issn = {2095-5138},
 journal = {{National Science Review}},
 doi = {10.1093/nsr/nwx105},
 file = {Zhang, Yang 2018 - An overview of multi-task learning:Attachments/Zhang, Yang 2018 - An overview of multi-task learning.pdf:application/pdf}
}


@inproceedings{Zhang.2018b,
 author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
 title = {{Mitigating Unwanted Biases with Adversarial Learning}},
 publisher = {ACM},
 booktitle = {{Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society}},
 year = {2018},
 address = {New York, NY, USA},
 doi = {10.1145/3278721.3278779},
 file = {Zhang, Lemoine et al. 2018 - Mitigating Unwanted Biases with Adversarial:Attachments/Zhang, Lemoine et al. 2018 - Mitigating Unwanted Biases with Adversarial.pdf:application/pdf}
}


@article{Zhao.2018,
 abstract = {In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.},
 author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei},
 year = {2018},
 title = {{Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods}},
 pages = {15--20},
 journal = {{Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2}},
 doi = {10.18653/v1/N18-2003},
 file = {Zhao, Wang et al. - Gender Bias in Coreference Resolution:Attachments/Zhao, Wang et al. - Gender Bias in Coreference Resolution.pdf:application/pdf}
}


@book{Zhao.2023,
 abstract = {The unequal representation of different groups in a sample population can lead to discrimination of minority groups when machine learning models make automated decisions. To address these issues, fairness-aware machine learning jointly optimizes two (or more) metrics aiming at predictive effectiveness and low unfairness. However, the inherent under-representation of minorities in the data makes the disparate treatment of subpopulations less noticeable and difficult to deal with during learning. In this paper, we propose a novel adversarial reweighting method to address such \emph{representation bias}. To balance the data distribution between the majority and the minority groups, our approach deemphasizes samples from the majority group. To minimize empirical risk, our method prefers samples from the majority group that are close to the minority group as evaluated by the Wasserstein distance. Our theoretical analysis shows the effectiveness of our adversarial reweighting approach. Experiments demonstrate that our approach mitigates bias without sacrificing classification accuracy, outperforming related state-of-the-art methods on image and tabular benchmark datasets.},
 author = {Zhao, Xuan and Fabbrizzi, Simone and Lobo, Paula Reyero and Ghodsi, Siamak and Broelemann, Klaus and Staab, Steffen and Kasneci, Gjergji},
publisher = {arXiv},
 year = {2023},
 title = {{Adversarial Reweighting Guided by Wasserstein Distance for Bias  Mitigation}},
 keywords = {Computer Science - Learning},
 file = {Zhao, Fabbrizzi et al. 2023 - Adversarial Reweighting Guided by Wasserstein (2):Attachments/Zhao, Fabbrizzi et al. 2023 - Adversarial Reweighting Guided by Wasserstein (2).pdf:application/pdf}
}


