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
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredIn the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Algorithms of oppression: how search engines reinforce racism
Polynczuk-Alenius, K. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2019.1593485 · Cited by 2,934
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887
Algorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681
Self-citationA Synopsis of Linguistic Theory, 1930-1955
Firth, J. R. (1957) · Medical Entomology and Zoology · Cited by 1,675
Fairness and Abstraction in Sociotechnical Systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287598 · Cited by 1,631
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615
Pervasiveness and correlates of implicit attitudes and stereotypes
Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., Smith, C. T., Olson, K. R., Chugh, D., Greenwald, A. G., et al. (2007) · European Review of Social Psychology · doi:10.1080/10463280701489053 · Cited by 1,247
Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720347115 · Cited by 1,065
The Risk of Racial Bias in Hate Speech Detection
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817
Measuring and Mitigating Unintended Bias in Text Classification
Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674
More than words: Social networks’ text mining for consumer brand sentiments
Mostafa, M. M. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.019 · Cited by 651
Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008) · Political Analysis · doi:10.1093/pan/mpn018 · Cited by 542
On Measuring Social Biases in Sentence Encoders
May, C., Wang, A., Bordia, S., Bowman, S. R., & Rudinger, R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1063 · Cited by 461
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Borkan, D., Dixon, L., Sorensen, J. S., Thain, N., & Vasserman, L. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3317593 · Cited by 419
Public awareness, attitudes and beliefs regarding intellectual disability: a systematic review
Scior, K. (2011) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2011.07.005 · Cited by 415
Racial Bias in Hate Speech and Abusive Language Detection Datasets
Davidson, T., Bhattacharya, D., & Weber, I. (2019) · Proceedings of the Third Workshop on Abusive Language Online · doi:10.18653/v1/w19-3504 · Cited by 414
Measuring Bias in Contextualized Word Representations
Kurita, K., Vyas, N., Pareek, A., Black, A. W., & Tsvetkov, Y. (2019) · Proceedings of the First Workshop on Gender Bias in Natural Language Processing · doi:10.18653/v1/w19-3823 · Cited by 348
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., & Kalai, A. T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287572 · Cited by 337
The social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329
Manzini, T., Chong, L. Y., Black, A. W., & Tsvetkov, Y. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1062 · Cited by 284
Delayed Impact of Fair Machine Learning
Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/862 · Cited by 265
Challenges and frontiers in abusive content detection
Vidgen, B., Harris, A., Nguyen, D., Tromble, R., Hale, S. A., & Margetts, H. Z. (2019) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w19-3509 · Cited by 217
Counterfactual Fairness in Text Classification through Robustness
Garg, S., Perot, V., Limtiaco, N., Taly, A., Ed, H., & Beutel, A. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3317950 · Cited by 200
The Social Cost of Strategic Classification
Milli, S., Miller, J. P., Dragan, A. D., & Hardt, M. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287576 · Cited by 168
Runaway Feedback Loops in Predictive Policing
Ensign, D., Friedler, S. A., Neville, S., Scheidegger, C., & Venkatasubramanian, S. (2017) · arXiv · doi:10.48550/arxiv.1706.09847 · Cited by 161
Crossmod: A Cross-Community Learning-based System to Assist Reddit Moderators
Chandrasekharan, E., Gandhi, C., Mustelier, M. W., & Gilbert, E. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359276 · Cited by 151
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse
Jurgens, D., Hemphill, L., & Chandrasekharan, E. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1357 · Cited by 140
Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Prabhakaran, V., Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1578 · Cited by 115
Self-citationThe Social Impact Of Natural Language Processing
Hovy, D., & Spruit, S. L. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1000925 · Cited by 80
The Social Impact Of Natural Language Processing
Hovy, D., & Spruit, S. L. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1000924 · Cited by 80
(2018) · Cited by 78
Hanson, V. L., Cavender, A. C., & Trewin, S. M. (2015) · interactions · doi:10.1145/2828432 · Cited by 57
RtGender: A Corpus for Studying Differential Responses to Gender
Voigt, R., Jurgens, D., Prabhakaran, V., Jurafsky, D., & Tsvetkov, Y. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3di79eiki43x · Cited by 52
Self-citationModeration Techniques for Social Media Content
Veglis, A. (2014) · Interacción · doi:10.1007/978-3-319-07632-4_13 · Cited by 40
The Linguistic Ideologies of Deep Abusive Language Classification
Castelle, M. (2018) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w18-5120 · Cited by 31
Toward Gender-Inclusive Coreference Resolution
Cao, Y. T., & Daumé, H., III (2020) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/2020.acl-main.418 · Cited by 22
Flagged first, then by citations
2 of the 43 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- 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).
- 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.
- 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).
- 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.
Cited by
Sorted by citations
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
Self-citationLanguage (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050
“…et al. (2019); Sun et al. (2019); Tan and Celis (2019); Webster et al. (2019); Zmigrod et al. (2019); Gyamfi et al. (2020); Hube et al. (2020); Hutchinson et al. (2020); Kim et al. (2020); Nadeem et al. (2020); Papakyriakopoulos et al. (2020); Ravfogel et al. (2020); Rozado (2020); Sen and…”
InfluentialLund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919
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 ›
Are you an author, or do you see a problem with this page? Request a review
