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The Risk of Racial Bias in Hate Speech Detection
Open accessAbstract
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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Cites
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Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610
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“Toxic language (e.g., hate speech, abusive speech, or other offensive speech) primarily targets members of minority groups and can catalyze real-life violence towards them (O’Keeffe et al., 2011; Cleland, 2014; Mozur, 2018).”
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Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
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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
Self-citationPaLM: 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
“However, this may have disproportionately excluded casual language, code-switching, or dialectal diversity (e.g., Sap et al. (2019)) and may limit PaLM’s capability to model the non-dominant dialects across the English-speaking regions globally.”
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“…associated with AAE than on text without these features (Jørgensen et al., 2015, 2016; Blodgett et al., 2016, 2018), and that toxicity detection systems score tweets containing features associated with AAE as more offensive than tweets without them (Davidson et al., 2019; Sap et al., 2019).”
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Racial disparities in automated speech recognition
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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
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