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Reducing Gender Bias in Abusive Language Detection

arXiv 1808.07231 ↗First posted 22 Aug 2018cs.CLcited by 152

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Abstract (arXiv)

Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, "You are a good woman" was considered "sexist" when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure gender biases on models trained with different abusive language datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three bias mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce gender bias by 90-98% and can be extended to correct model bias in other scenarios.

Key facts. Cited by 152 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2018.

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Citing papers per year: 2021: 37; 2022: 42; 2023: 34; 2024: 19; 2025: 16; 2026: 4.

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  1. 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 477
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  4. 2021Societal Biases in Language Generation: Progress and Challengescited by 201
  5. 2023Gender bias and stereotypes in Large Language Modelscited by 185
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  8. 2021Challenges in Automated Debiasing for Toxic Language Detectioncited by 95
  9. 2021Trustworthy AI: A Computational Perspectivecited by 93
  10. 2023A Survey on Fairness in Large Language Modelscited by 91
  11. 2024Large Language Models are Geographically Biasedcited by 91
  12. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
38 more
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  2. 2021On Measures of Biases and Harms in NLPcited by 81
  3. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  4. 2021Mitigating Political Bias in Language Models Through Reinforced Calibrationcited by 73
  5. 2021Towards generalisable hate speech detection: a review on obstacles and solutionscited by 66
  6. 2022Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderationcited by 66
  7. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  8. 2022Handling Bias in Toxic Speech Detection: A Surveycited by 60
  9. 2022Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Listscited by 54
  10. 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
  11. 2021ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AIcited by 46
  12. 2022MoCoDA: Model-based Counterfactual Data Augmentationcited by 44
  13. 2023Evaluating GPT-3 Generated Explanations for Hateful Content Moderationcited by 44
  14. 2021Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communitiescited by 40
  15. 2022Quantifying Social Biases Using Templates is Unreliablecited by 38
  16. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  17. 2021Balancing out Bias: Achieving Fairness Through Balanced Trainingcited by 34
  18. 2022Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalizationcited by 34
  19. 2022Fairness Reprogrammingcited by 33
  20. 2023Leveraging sparse and shared feature activations for disentangled representation learningcited by 32
  21. 2021Detecting Cross-Geographic Biases in Toxicity Modeling on Social Mediacited by 31
  22. 2021Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lenscited by 30
  23. 2022Choose Your Lenses: Flaws in Gender Bias Evaluationcited by 29
  24. 2021Evaluating Gender Bias in Natural Language Inferencecited by 27
  25. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 27
  26. 2021Ruddit: Norms of Offensiveness for English Reddit Commentscited by 26
  27. 2022Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refittingcited by 26
  28. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  29. 2023CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Modelscited by 25
  30. 2024From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasetscited by 23
  31. 2022Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairnesscited by 21
  32. 2021Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classificationcited by 20
  33. 2022On Modality Bias Recognition and Reductioncited by 20
  34. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  35. 2021Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Frameworkcited by 19
  36. 2024NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helpscited by 19
  37. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  38. 2021The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classificationcited by 18

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