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Social Biases in NLP Models as Barriers for Persons with Disabilities
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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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“From negative sentiment and toxicity directed towards some social groups, to stereotypical linguistic associations, to lack of recognition of certain language dialects, the presence of biases of LLMs have been well-documented ( e.g. , Blodgett and O'Connor 2017; Hutchinson et al. 2020; Mei, Fereidooni, and Caliskan 2023; Mˇ echura 2022; Mozafari, Farahbakhsh, and Crespi 2020; Sap et al. 2019; Shen”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Hutchinson et al. 2020
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“Text suggestions based on language models have been known to inherit biases and dominant 'views' from the text corpus used to train the language model [22,39,43,55].”
From Interacting with Next-Phrase Suggestions: How Suggestion Systems Aid and Influence the Cognitive Processes of Writing (2023) · cites this paper as [39]
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“LLMs learn from web-sourced text data, acquiring inherent biases (31–33) and—in some cases—replicate excerpts from their training data (34).”
From How should the advancement of large language models affect the practice of science? (2025) · cites this paper as [33]
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Multimodal large language models for inclusive collaboration learning tasks
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“Despite significant efforts on the part of researchers developing LLMs, there remain issues around bias (Santurkar et al., 2023; Hutchinson et al., 2020; Kurita et al., 2019; Basta et al., 2019; Abid et al., 2021b; Sap et al., 2020), prompt injection attacks (Greshake et al.”
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Metadata
- Authors
- Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., & Denuyl, S.
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 5491-5501
- ISSN
- 0736-587X (ISSN-L)
- 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
- hybrid
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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