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The Risk of Racial Bias in Hate Speech Detection
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We investigate how annotators' insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations.We first uncover unexpected correlations between surface markers of African American English (AAE) and ratings of toxicity in several widely-used hate speech datasets.Then, we show that models trained on these corpora acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others.Finally, we propose dialect and race priming as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet's dialect they are significantly less likely to label the tweet as offensive.
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“Toxicity detection models misclassify African-American English tweets as negative more often than those written in Standard American English (Mozafari, Farahbakhsh, and Crespi 2020; Sap et al. 2019).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Sap et al. 2019
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“For example, toxicity classifiers may disproportionately flag African-American English (Mozafari, Farahbakhsh, and Crespi 2020; Sap et al. 2019)”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Sap et al. 2019
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“models are trained with historical data and can present issues such as gender bias and racial bias in AI models [68,73], potentially exacerbating structural inequalities [10]”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [73]
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Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
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Multi-Dimensional Gender Bias Classification
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Metadata
- Authors
- Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A.
- Affiliations
- University of Washington (US)
- Carnegie Mellon University (US)
- Allen Institute for Artificial Intelligence (US)
- Corresponding author
- Sap, Maarten
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 1668-1678
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2949678053
- Cited by
- 817
- References
- 40
- Citations by year
- 2026: 59 · 2025: 96 · 2024: 86 · 2023: 139 · 2022: 142 · 2021: 178 · 2020: 105 · 2019: 12
- FWCI
- 40.99
- 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)
- Funders
- National Science Foundation (1714566)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- hate speech detection, racial bias, African American English, dialects, annotation bias, hate speech datasets, priming, minority groups
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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