mlsanityData to 14 Oct 2026

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes

arXiv 1904.05233 ↗First posted 10 Apr 2019cs.LG cs.CL stat.MLcited by 51

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Abstract (arXiv)

There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is often desirable to simultaneously consider multiple protected attributes, as well as their intersections. In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual's true occupation and a word embedding of their name. This method leverages the societal biases that are encoded in word embeddings, eliminating the need for access to protected attributes. Crucially, it only requires access to individuals' names at training time and not at deployment time. We evaluate two variations of our proposed method using a large-scale dataset of online biographies. We find that both variations simultaneously reduce race and gender biases, with almost no reduction in the classifier's overall true positive rate.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Apr 2019.

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Citing papers per year: 2021: 11; 2022: 14; 2023: 10; 2024: 8; 2025: 2; 2026: 6.

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  1. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
  2. 2022Impact of Pretraining Term Frequencies on Few-Shot Reasoningcited by 142
  3. 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 91
  4. 2021Contrastive Explanations for Model Interpretabilitycited by 75
  5. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 75
  6. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  7. 2022Gender Bias in Masked Language Models for Multiple Languagescited by 60
  8. 2022Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Listscited by 55
  9. 2021Disembodied Machine Learning: On the Illusion of Objectivity in NLPcited by 37
  10. 2021Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankerscited by 35
  11. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 33
  12. 2023On the Independence of Association Bias and Empirical Fairness in Language Modelscited by 29
38 more
  1. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 28
  2. 2023Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projectioncited by 25
  3. 2021Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelscited by 23
  4. 2024Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLPcited by 22
  5. 2021Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classificationcited by 20
  6. 2024Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)cited by 20
  7. 2022Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projectioncited by 17
  8. 2024From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguardscited by 17
  9. 2022Investigations of Performance and Bias in Human-AI Teamwork in Hiringcited by 16
  10. 2022Learning Fair Representations via Rate-Distortion Maximizationcited by 15
  11. 2023Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuningcited by 15
  12. 2021Statistical inference for individual fairnesscited by 13
  13. 2022Domain Adaptation meets Individual Fairness. And they get alongcited by 13
  14. 2023Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusioncited by 13
  15. 2024Will the Real Linda Please Stand up...to Large Language Models? Examining the Representativeness Heuristic in LLMscited by 13
  16. 2025Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitmentcited by 13
  17. 2022How Gender Debiasing Affects Internal Model Representations, and Why It Matterscited by 12
  18. 2023Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPcited by 11
  19. 2023"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviorscited by 11
  20. 2022Fair NLP Models with Differentially Private Text Encoderscited by 9
  21. 2024Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapterscited by 8
  22. 2024Distributionally Generative Augmentation for Fair Facial Attribute Classificationcited by 8
  23. 2021Social Norm Bias: Residual Harms of Fairness-Aware Algorithmscited by 6
  24. 2021Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognitioncited by 5
  25. 2022Subverting Fair Image Search with Generative Adversarial Perturbationscited by 5
  26. 2022Sustaining Fairness via Incremental Learningcited by 5
  27. 2024Addressing Both Statistical and Causal Gender Fairness in NLP Modelscited by 4
  28. 2025On the Mutual Influence of Gender and Occupation in LLM Representationscited by 4
  29. 2022Speciesist Language and Nonhuman Animal Bias in English Masked Language Modelscited by 2
  30. 2021Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion and Sentiment Classificationcited by 1
  31. 2023Reducing Sensitivity on Speaker Names for Text Generation from Dialoguescited by 1
  32. 2024Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Modelscited by 1
  33. 2026Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Managercited by 1
  34. 2023Transferring Fairness using Multi-Task Learning with Limited Demographic Informationcited by 0
  35. 2026Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profilescited by 0
  36. 2026Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learningcited by 0
  37. 2026The Fairness Collapse Phenomenon: Bias Amplification in Language Models Trained on Synthetic Datacited by 0
  38. 2026No PUN Intended: Plausible Unknown Names for Person-Centred LLM Evaluationcited by 0

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