Marta Marchiori Manerba

Also published as: Marta Marchiori Manerba


2024

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FairBelief - Assessing Harmful Beliefs in Language Models
Mattia Setzu | Marta Marchiori Manerba | Pasquale Minervini | Debora Nozza
Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024)

Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world applications without careful fairness auditing.This paper proposes FairBelief, an analytical approach to capture and assess beliefs, i.e., propositions that an LM may embed with different degrees of confidence and that covertly influence its predictions. With FairBelief, we leverage prompting to study the behavior of several state-of-the-art LMs across different previously neglected axes, such as model scale and likelihood, assessing predictions on a fairness dataset specifically designed to quantify LMs’ outputs’ hurtfulness.Finally, we conclude with an in-depth qualitative assessment of the beliefs emitted by the models.We apply FairBelief to English LMs, revealing that, although these architectures enable high performances on diverse natural language processing tasks, they show hurtful beliefs about specific genders. Interestingly, training procedure and dataset, model scale, and architecture induce beliefs of different degrees of hurtfulness.

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An Overview of Recent Approaches to Enable Diversity in Large Language Models through Aligning with Human Perspectives
Benedetta Muscato | Chandana Sree Mala | Marta Marchiori Manerba | Gizem Gezici | Fosca Giannotti
Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024

The varied backgrounds and experiences of human annotators inject different opinions and potential biases into the data, inevitably leading to disagreements. Yet, traditional aggregation methods fail to capture individual judgments since they rely on the notion of a single ground truth. Our aim is to review prior contributions to pinpoint the shortcomings that might cause stereotypical content generation. As a preliminary study, our purpose is to investigate state-of-the-art approaches, primarily focusing on the following two research directions. First, we investigate how adding subjectivity aspects to LLMs might guarantee diversity. We then look into the alignment between humans and LLMs and discuss how to measure it. Considering existing gaps, our review explores possible methods to mitigate the perpetuation of biases targeting specific communities. However, we recognize the potential risk of disseminating sensitive information due to the utilization of socio-demographic data in the training process. These considerations underscore the inclusion of diverse perspectives while taking into account the critical importance of implementing robust safeguards to protect individuals’ privacy and prevent the inadvertent propagation of sensitive information.

2022

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Bias Discovery within Human Raters: A Case Study of the Jigsaw Dataset
Marta Marchiori Manerba | Riccardo Guidotti | Lucia Passaro | Salvatore Ruggieri
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

Understanding and quantifying the bias introduced by human annotation of data is a crucial problem for trustworthy supervised learning. Recently, a perspectivist trend has emerged in the NLP community, focusing on the inadequacy of previous aggregation schemes, which suppose the existence of single ground truth. This assumption is particularly problematic for sensitive tasks involving subjective human judgments, such as toxicity detection. To address these issues, we propose a preliminary approach for bias discovery within human raters by exploring individual ratings for specific sensitive topics annotated in the texts. Our analysis’s object consists of the Jigsaw dataset, a collection of comments aiming at challenging online toxicity identification.

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LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide
Arianna Muti | Marta Marchiori Manerba | Katerina Korre | Alberto Barrón-Cedeño
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task i and applied it to the dataset for Task j to iteratively integrate new silver data for Task i. Our official submissions to the shared task obtained a macro-averaged F1 score of 0.53 for Hope Speech and 0.46 for Homo/Transphobia, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.

2021

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Fine-Grained Fairness Analysis of Abusive Language Detection Systems with CheckList
Marta Marchiori Manerba | Sara Tonelli
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

Current abusive language detection systems have demonstrated unintended bias towards sensitive features such as nationality or gender. This is a crucial issue, which may harm minorities and underrepresented groups if such systems were integrated in real-world applications. In this paper, we create ad hoc tests through the CheckList tool (Ribeiro et al., 2020) to detect biases within abusive language classifiers for English. We compare the behaviour of two BERT-based models, one trained on a generic hate speech dataset and the other on a dataset for misogyny detection. Our evaluation shows that, although BERT-based classifiers achieve high accuracy levels on a variety of natural language processing tasks, they perform very poorly as regards fairness and bias, in particular on samples involving implicit stereotypes, expressions of hate towards minorities and protected attributes such as race or sexual orientation. We release both the notebooks implemented to extend the Fairness tests and the synthetic datasets usable to evaluate systems bias independently of CheckList.