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Cited by 301Ethics and Social Impacts of AIHate Speech and Cyberbullying DetectionDisability Rights and Representation

The DOI matches its Crossref record; 41 of the 43 references it cites were found in a registry or an index.

Social Biases in NLP Models as Barriers for Persons with Disabilities

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Hutchinson, Ben, Prabhakaran, Vinodkumar, Denton, Emily, Webster, Kellie, Zhong, Yu, Denuyl, Stephen·2020·Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
DOI 10.18653/v1/2020.acl-main.487
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Abstract

Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models.In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from the data on which they are trained.In this paper, we present evidence of such undesirable biases towards mentions of disability in two different English language models: toxicity prediction and sentiment analysis.Next, we demonstrate that the neural embeddings that are the critical first step in most NLP pipelines similarly contain undesirable biases towards mentions of disability.We end by highlighting topical biases in the discourse about disability which may contribute to the observed model biases; for instance, gun violence, homelessness, and drug addiction are over-represented in texts discussing mental illness.

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  1. Su Lin Blodgett, Solon Barocas, Hal III Daume, and Hanna Wallach. 2020. Language (technology) is power: The need to be explicit about NLP harms. In Proceedings of the Annual Meeting of the Associ- ation for Computational Lingustics (ACL).
  2. Alexander D'Amour, Hansa Srinivasan, James At- wood, Pallavi Baljekar, D Sculley, and Yoni Halpern. 2020. Fairness is not static: Deeper understanding of long term fairness via simulation studies. In Pro- ceedings of the 2020 Conference on Fairness, Ac- countability, and Transparency, pages 525-534.
  3. Donald Martin Jr., Vinodkumar Prabhakaran, Jill Kuhlberg, Andrew Smart, and William Isaac. 2020. Participatory problem formulation for fairer ma- chine learning through community based system dy- namics approach. In ICLR Workshop on Machine Learning in Real Life (ML-IRL).
  4. Deirdre K Mulligan, Joshua A Kroll, Nitin Kohli, and Richmond Y Wong. 2019. This thing called fairness: Disciplinary confusion realizing a value in technol- ogy. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1-36.

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Metadata

Authors
Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., & Denuyl, S.
ORCID
Venue
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics
Published
2020
Pages
5491-5501
ISSN
0736-587X (ISSN-L)
DOI
10.18653/v1/2020.acl-main.487
OpenAlex
W3035296331
Cited by
274
References
43
Citations by year
2026: 21 · 2025: 41 · 2024: 44 · 2023: 66 · 2022: 44 · 2021: 48 · 2020: 10
FWCI
43.56
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Social Sciences › Sociology and Political Science › Disability Rights and Representation
Keywords
social bias, disability, natural language processing models, toxicity prediction, sentiment analysis, neural embeddings, bias in language models, mental illness discourse, inclusive technology, disability representation

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