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Demoting Racial Bias in Hate Speech Detection

arXiv 2005.12246 ↗First posted 25 May 2020cs.CLcited by 62

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Abstract (arXiv)

In current hate speech datasets, there exists a high correlation between annotators' perceptions of toxicity and signals of African American English (AAE). This bias in annotated training data and the tendency of machine learning models to amplify it cause AAE text to often be mislabeled as abusive/offensive/hate speech with a high false positive rate by current hate speech classifiers. In this paper, we use adversarial training to mitigate this bias, introducing a hate speech classifier that learns to detect toxic sentences while demoting confounds corresponding to AAE texts. Experimental results on a hate speech dataset and an AAE dataset suggest that our method is able to substantially reduce the false positive rate for AAE text while only minimally affecting the performance of hate speech classification.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 22; 2023: 4; 2024: 6; 2025: 6; 2026: 2.

62 papers.

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  1. 2021Latent Hatred: A Benchmark for Understanding Implicit Hate Speechcited by 216
  2. 2021Challenges in Automated Debiasing for Toxic Language Detectioncited by 95
  3. 2021MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targetscited by 85
  4. 2021On Measures of Biases and Harms in NLPcited by 81
  5. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  6. 2021Anticipating Safety Issues in E2E Conversational AI: Framework and Toolingcited by 77
  7. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  8. 2021Towards generalisable hate speech detection: a review on obstacles and solutionscited by 66
  9. 2022Handling Bias in Toxic Speech Detection: A Surveycited by 60
  10. 2021Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communitiescited by 40
  11. 2022Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Counteringcited by 40
  12. 2023Evaluation of African American Language Bias in Natural Language Generationcited by 36
38 more
  1. 2022Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Studycited by 30
  2. 2021Modeling the Second Player in Distributionally Robust Optimizationcited by 29
  3. 2022On Explaining Multimodal Hateful Meme Detection Modelscited by 29
  4. 2021An Information-theoretic Approach to Distribution Shiftscited by 25
  5. 2023Parameter-efficient Modularised Bias Mitigation via AdapterFusioncited by 25
  6. 2024What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detectioncited by 24
  7. 2021Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Frameworkcited by 19
  8. 2022Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworkscited by 18
  9. 2022Controlling Bias Exposure for Fair Interpretable Predictionscited by 18
  10. 2021Countering Online Hate Speech: An NLP Perspectivecited by 12
  11. 2021Hate Speech Classifiers Learn Human-Like Social Stereotypescited by 12
  12. 2022Detecting Unintended Social Bias in Toxic Language Datasetscited by 12
  13. 2025Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variationscited by 12
  14. 2021ToxCCIn: Toxic Content Classification with Interpretabilitycited by 10
  15. 2024BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Banglacited by 10
  16. 2021From Toxicity in Online Comments to Incivility in American News: Proceed with Cautioncited by 8
  17. 2022Automated Identification of Toxic Code Reviews Using ToxiCRcited by 8
  18. 2022Mitigating Gender Bias in Machine Translation through Adversarial Learningcited by 8
  19. 2022Detecting Harmful Online Conversational Content towards LGBTQIA+ Individualscited by 8
  20. 2022HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Modelscited by 8
  21. 2021Mitigating Biases in Toxic Language Detection through Invariant Rationalizationcited by 7
  22. 2022Analyzing Hate Speech Data along Racial, Gender and Intersectional Axescited by 7
  23. 2023Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Modelscited by 7
  24. 2021Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputationcited by 6
  25. 2021Personalized Benchmarking with the Ludwig Benchmarking Toolkitcited by 6
  26. 2022Toward Understanding Bias Correlations for Mitigation in NLPcited by 5
  27. 2021Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directionscited by 4
  28. 2022Manifestations of Xenophobia in AI Systemscited by 4
  29. 2022Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speechcited by 3
  30. 2022Enriching Abusive Language Detection with Community Contextcited by 3
  31. 2022Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American Englishcited by 3
  32. 2022Harnessing Knowledge and Reasoning for Human-Like Natural Language Generation: A Brief Reviewcited by 3
  33. 2023Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Modelscited by 3
  34. 2024Analyzing Toxicity in Deep Conversations: A Reddit Case Studycited by 3
  35. 2025Data Caricatures: On the Representation of African American Language in Pretraining Corporacited by 3
  36. 2022Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inferencecited by 2
  37. 2024Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networkscited by 2
  38. 2025Don't Erase, Inform! Detecting and Contextualizing Harmful Language in Cultural Heritage Collectionscited by 2

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