Abstract
This paper is a summary of the work done in my PhD thesis. Where I investigate the impact of bias in NLP models on the task of hate speech detection from three perspectives: explainability, offensive stereotyping bias, and fairness. Then, I discuss the main takeaways from my thesis and how they can benefit the broader NLP community. Finally, I discuss important future research directions. The findings of my thesis suggest that the bias in NLP models impacts the task of hate speech detection from all three perspectives. And that unless we start incorporating social sciences in studying bias in NLP models, we will not effectively overcome the current limitations of measuring and mitigating bias in NLP models.- Anthology ID:
- 2023.bigpicture-1.5
- Volume:
- Proceedings of the Big Picture Workshop
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Yanai Elazar, Allyson Ettinger, Nora Kassner, Sebastian Ruder, Noah A. Smith
- Venue:
- BigPicture
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 53–65
- Language:
- URL:
- https://aclanthology.org/2023.bigpicture-1.5
- DOI:
- 10.18653/v1/2023.bigpicture-1.5
- Cite (ACL):
- Fatma Elsafoury. 2023. Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection. In Proceedings of the Big Picture Workshop, pages 53–65, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection (Elsafoury, BigPicture 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.bigpicture-1.5.pdf