mlsanityData to 14 Oct 2026

Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

arXiv 1903.03862 ↗First posted 9 Mar 2019cs.CLcited by 268

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Abstract (arXiv)

Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between "gender-neutralized" words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.

Key facts. Cited by 268 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Mar 2019.

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Citing papers per year: 2021: 59; 2022: 60; 2023: 48; 2024: 51; 2025: 28; 2026: 22.

268 papers.

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  1. 2024Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMscited by 735
  2. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  3. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 337
  4. 2021What Will it Take to Fix Benchmarking in Natural Language Understanding?cited by 203
  5. 2022On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoningcited by 194
  6. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
  7. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  8. 2023Debiasing Vision-Language Models via Biased Promptscited by 149
  9. 2021Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Modelscited by 141
  10. 2023Large language models can accurately predict searcher preferencescited by 131
  11. 2024An Adversarial Perspective on Machine Unlearning for AI Safetycited by 123
  12. 2022Pretrained Language Models for Text Generation: A Surveycited by 122
38 more
  1. 2021Gender Bias in Machine Translationcited by 117
  2. 2021Debiasing Pre-trained Contextualised Embeddingscited by 107
  3. 2022A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learningcited by 104
  4. 2022XAI for Transformers: Better Explanations through Conservative Propagationcited by 100
  5. 2025LLM Social Simulations Are a Promising Research Methodcited by 99
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  8. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  9. 2021On Measures of Biases and Harms in NLPcited by 81
  10. 2021What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpuscited by 80
  11. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  12. 2023Concept Algebra for (Score-Based) Text-Controlled Generative Modelscited by 59
  13. 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
  14. 2021BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?cited by 56
  15. 2023Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Biascited by 53
  16. 2022Perturbation Augmentation for Fairer NLPcited by 52
  17. 2024Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactionscited by 51
  18. 2024Representation Surgery: Theory and Practice of Affine Steeringcited by 50
  19. 2022Kernelized Concept Erasurecited by 48
  20. 2022Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Taskscited by 48
  21. 2022Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gendercited by 47
  22. 2021Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?cited by 46
  23. 2021NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternativescited by 46
  24. 2021They, Them, Theirs: Rewriting with Gender-Neutral Englishcited by 44
  25. 2024Who's asking? User personas and the mechanics of latent misalignmentcited by 40
  26. 2023Bias of AI-Generated Content: An Examination of News Produced by Large Language Modelscited by 38
  27. 2021Disembodied Machine Learning: On the Illusion of Objectivity in NLPcited by 37
  28. 2023Mass-Producing Failures of Multimodal Systems with Language Modelscited by 36
  29. 2021Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgettingcited by 35
  30. 2022Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Informationcited by 34
  31. 2024Stereotype or Personalization? User Identity Biases Chatbot Recommendationscited by 34
  32. 2021Uncovering Implicit Gender Bias in Narratives through Commonsense Inferencecited by 32
  33. 2024On the Challenges and Opportunities in Generative AIcited by 31
  34. 2021Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lenscited by 30
  35. 2024Applied Causal Inference Powered by ML and AIcited by 30
  36. 2022Choose Your Lenses: Flaws in Gender Bias Evaluationcited by 29
  37. 2023On the Independence of Association Bias and Empirical Fairness in Language Modelscited by 29
  38. 2021Fair Representation Learning for Heterogeneous Information Networkscited by 28

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