mlsanityData to 7 Oct 2026

Does Gender Matter? Towards Fairness in Dialogue Systems

arXiv 1910.10486 ↗First posted 16 Oct 2019cs.CL cs.AIcited by 89

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Abstract (arXiv)

Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recognition algorithms in computer vision are unfair to black people such as poorly detecting their faces and inappropriately identifying them as "gorillas". As one crucial application of AI, dialogue systems have been extensively applied in our society. They are usually built with real human conversational data; thus they could inherit some fairness issues which are held in the real world. However, the fairness of dialogue systems has not been well investigated. In this paper, we perform a pioneering study about the fairness issues in dialogue systems. In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models. Our studies demonstrate that popular dialogue models show significant prejudice towards different genders and races. Besides, to mitigate the bias in dialogue systems, we propose two simple but effective debiasing methods. Experiments show that our methods can reduce the bias in dialogue systems significantly. The dataset and the implementation are released to foster fairness research in dialogue systems.

Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2019.

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Citing papers per year: 2021: 19; 2022: 21; 2023: 20; 2024: 18; 2025: 9; 2026: 2.

89 papers.

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  11. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  12. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
38 more
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  5. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  6. 2022COLD: A Benchmark for Chinese Offensive Language Detectioncited by 74
  7. 2021Few-Shot Bot: Prompt-Based Learning for Dialogue Systemscited by 64
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  9. 2024Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypescited by 58
  10. 2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Modelcited by 57
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  12. 2023BiasAsker: Measuring the Bias in Conversational AI Systemcited by 52
  13. 2023Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systemscited by 37
  14. 2022Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarkscited by 36
  15. 2021Revealing Persona Biases in Dialogue Systemscited by 33
  16. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  17. 2021Uncovering Implicit Gender Bias in Narratives through Commonsense Inferencecited by 32
  18. 2022Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimizationcited by 32
  19. 2024MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMscited by 32
  20. 2023On the Independence of Association Bias and Empirical Fairness in Language Modelscited by 29
  21. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 27
  22. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  23. 2023CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Modelscited by 25
  24. 2021Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelscited by 23
  25. 2021Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decodingcited by 23
  26. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
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  28. 2021Assessing Political Prudence of Open-domain Chatbotscited by 20
  29. 2021The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classificationcited by 18
  30. 2021Jointly Attacking Graph Neural Network and its Explanationscited by 18
  31. 2024Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Biascited by 17
  32. 2021The Arabic Parallel Gender Corpus 2.0: Extensions and Analysescited by 12
  33. 2023Learning to Generate Equitable Text in Dialogue from Biased Training Datacited by 12
  34. 2024Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approachcited by 12
  35. 2023Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societiescited by 11
  36. 2024Potential and Challenges of Model Editing for Social Debiasingcited by 10
  37. 2022PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Modelcited by 9
  38. 2024No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Userscited by 9

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