mlsanityData to 14 Oct 2026

On the Apparent Conflict Between Individual and Group Fairness

arXiv 1912.06883 ↗First posted 14 Dec 2019cs.LG cs.CY stat.MLcited by 126

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Abstract (arXiv)

A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on a misconception. It draws on theoretical discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fundamentally in conflict. First, it outlines accounts of egalitarian fairness which encompass plausible motivations for both group and individual fairness, thereby suggesting that there need be no conflict in principle. Second, it considers the concept of individual justice, from legal philosophy and jurisprudence which seems similar but actually contradicts the notion of individual fairness as proposed in the fair machine learning literature. The conclusion is that the apparent conflict between individual and group fairness is more of an artifact of the blunt application of fairness measures, rather than a matter of conflicting principles. In practice, this conflict may be resolved by a nuanced consideration of the sources of `unfairness' in a particular deployment context, and the carefully justified application of measures to mitigate it.

Key facts. Cited by 126 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 18; 2022: 30; 2023: 30; 2024: 20; 2025: 22; 2026: 6.

126 papers.

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  1. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  2. 2022Fairness in Graph Mining: A Surveycited by 76
  3. 2023Fast Model Debias with Machine Unlearningcited by 72
  4. 2022Learning Fair Node Representations with Graph Counterfactual Fairnesscited by 58
  5. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  6. 2021Characterizing Intersectional Group Fairness with Worst-Case Comparisonscited by 42
  7. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  8. 2021Assessing Multilingual Fairness in Pre-trained Multimodal Representationscited by 35
  9. 2023Inductive Graph Unlearningcited by 31
  10. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  11. 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairnesscited by 26
  12. 2023Survey of Social Bias in Vision-Language Modelscited by 26
38 more
  1. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  2. 2022Robust Explanation Constraints for Neural Networkscited by 24
  3. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  4. 2023Improving Recommendation Fairness via Data Augmentationcited by 18
  5. 2022CertiFair: A Framework for Certified Global Fairness of Neural Networkscited by 17
  6. 2023Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networkscited by 17
  7. 2021Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AIcited by 16
  8. 2022Accurate Fairness: Improving Individual Fairness without Trading Accuracycited by 16
  9. 2023Fairness through Aleatoric Uncertaintycited by 16
  10. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  11. 2021Unified Group Fairness on Federated Learningcited by 15
  12. 2021Fair Enough: Searching for Sufficient Measures of Fairnesscited by 14
  13. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
  14. 2023Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Treecited by 14
  15. 2022The Long Arc of Fairness: Formalisations and Ethical Discoursecited by 13
  16. 2022Optimal Transport of Classifiers to Fairnesscited by 12
  17. 2022Towards Involving End-users in Interactive Human-in-the-loop AI Fairnesscited by 12
  18. 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
  19. 2021fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigationcited by 9
  20. 2022Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulationcited by 9
  21. 2024AIM: Attributing, Interpreting, Mitigating Data Unfairnesscited by 9
  22. 2021BeFair: Addressing Fairness in the Banking Sectorcited by 8
  23. 2021What will it take to generate fairness-preserving explanations?cited by 7
  24. 2022Verifying Fairness in Quantum Machine Learningcited by 7
  25. 2023On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Modelcited by 7
  26. 2021End-To-End Bias Mitigation: Removing Gender Bias in Deep Learningcited by 6
  27. 2022Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learningcited by 6
  28. 2023DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervisioncited by 6
  29. 2023Causality-Aided Trade-off Analysis for Machine Learning Fairnesscited by 6
  30. 2024On the (In)Compatibility between Group Fairness and Individual Fairnesscited by 6
  31. 2021A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasetscited by 5
  32. 2022iFlipper: Label Flipping for Individual Fairnesscited by 5
  33. 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Datacited by 5
  34. 2024Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairnesscited by 5
  35. 2024What is Fair? Defining Fairness in Machine Learning for Healthcited by 5
  36. 2024Achieving Fairness Across Local and Global Models in Federated Learningcited by 5
  37. 2024EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholderscited by 5
  38. 2025Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspectivecited by 5

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