Bias Against 93 Stigmatized Groups in Masked Language Models and Downstream Sentiment Classification Tasks-Reference-Cited by-同舟云学术

Bias Against 93 Stigmatized Groups in Masked Language Models and Downstream Sentiment Classification Tasks

Published:2023-06-12 Issue: Volume: Page:
ISSN:
Container-title:2023 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Mei Katelyn¹^ORCID,Fereidooni Sonia²^ORCID,Caliskan Aylin¹^ORCID

Affiliation:

1. Information School, University of Washington, USA

2. University of Washington, USA

Funder

National Institute of Standards and Technology

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3593013.3594109

Reference58 articles.

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4. Su Lin Blodgett , Solon Barocas , Hal Daumé III, and Hanna Wallach . 2020 . Language (technology) is power: A critical survey of" bias" in nlp. arXiv preprint arXiv:2005.14050 (2020). Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020. Language (technology) is power: A critical survey of" bias" in nlp. arXiv preprint arXiv:2005.14050 (2020).

5. Shikha Bordia and Samuel R. Bowman . 2019. Identifying and Reducing Gender Bias in Word-Level Language Models . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics , Minneapolis, Minnesota, 7–15. https://doi.org/10. 1865 3/v1/N 19 - 3002 10.18653/v1 Shikha Bordia and Samuel R. Bowman. 2019. Identifying and Reducing Gender Bias in Word-Level Language Models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics, Minneapolis, Minnesota, 7–15. https://doi.org/10.18653/v1/N19-3002

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