mlsanityData to 14 Oct 2026

Fairness risk measures

arXiv 1901.08665 ↗First posted 24 Jan 2019cs.LG stat.MLcited by 85

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Abstract (arXiv)

Ensuring that classifiers are non-discriminatory or fair with respect to a sensitive feature (e.g., race or gender) is a topical problem. Progress in this task requires fixing a definition of fairness, and there have been several proposals in this regard over the past few years. Several of these, however, assume either binary sensitive features (thus precluding categorical or real-valued sensitive groups), or result in non-convex objectives (thus adversely affecting the optimisation landscape). In this paper, we propose a new definition of fairness that generalises some existing proposals, while allowing for generic sensitive features and resulting in a convex objective. The key idea is to enforce that the expected losses (or risks) across each subgroup induced by the sensitive feature are commensurate. We show how this relates to the rich literature on risk measures from mathematical finance. As a special case, this leads to a new convex fairness-aware objective based on minimising the conditional value at risk (CVaR).

Key facts. Cited by 85 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jan 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 18; 2022: 17; 2023: 16; 2024: 17; 2025: 9; 2026: 8.

85 papers.

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  1. 2021Label-Imbalanced and Group-Sensitive Classification under Overparameterizationcited by 74
  2. 2024Preserving Fairness Generalization in Deepfake Detectioncited by 70
  3. 2022AutoBalance: Optimized Loss Functions for Imbalanced Datacited by 49
  4. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  5. 2023Improving Fairness in Deepfake Detectioncited by 40
  6. 2021Selective Regression Under Fairness Criteriacited by 34
  7. 2023On the Independence of Association Bias and Empirical Fairness in Language Modelscited by 29
  8. 2024AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmarkcited by 28
  9. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  10. 2022Rank-based Decomposable Losses in Machine Learning: A Surveycited by 26
  11. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  12. 2021Teacher's pet: understanding and mitigating biases in distillationcited by 21
38 more
  1. 2022Conformalized Fairness via Quantile Regressioncited by 19
  2. 2021Fairness-Aware PAC Learning from Corrupted Datacited by 18
  3. 2021Optimal Algorithms for Differentially Private Stochastic Monotone Variational Inequalities and Saddle-Point Problemscited by 17
  4. 2022Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyondcited by 17
  5. 2023Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Modelscited by 17
  6. 2021Dynamic Forecasting of Conversation Derailmentcited by 16
  7. 2022Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcarecited by 16
  8. 2023Equal Opportunity of Coverage in Fair Regressioncited by 16
  9. 2022Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictionscited by 14
  10. 2024Domain Generalisation via Imprecise Learningcited by 14
  11. 2022Optimizing generalized Gini indices for fairness in rankingscited by 13
  12. 2023Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gapcited by 12
  13. 2023Being Right for Whose Right Reasons?cited by 12
  14. 2022The Impact of Differential Privacy on Group Disparity Mitigationcited by 11
  15. 2023Loss Balancing for Fair Supervised Learningcited by 11
  16. 2023On the Richness of Calibrationcited by 9
  17. 2021Fairness Through Regularization for Learning to Rankcited by 8
  18. 2022Stochastic Optimization for Spectral Risk Measurescited by 7
  19. 2021Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplerscited by 6
  20. 2022Metrizing Fairnesscited by 6
  21. 2022Malign Overfitting: Interpolation Can Provably Preclude Invariancecited by 6
  22. 2023Distribution-Free Statistical Dispersion Control for Societal Applicationscited by 6
  23. 2025Preserving AUC Fairness in Learning with Noisy Protected Groupscited by 6
  24. 2022Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learningcited by 5
  25. 2023A Model-Based Method for Minimizing CVaR and Beyondcited by 5
  26. 2023BSL: Understanding and Improving Softmax Loss for Recommendationcited by 5
  27. 2024Evaluating Model Performance Under Worst-case Subpopulationscited by 5
  28. 2024Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsitycited by 5
  29. 2021A Survey of Learning Criteria Going Beyond the Usual Riskcited by 4
  30. 2022Fair Wrapping for Black-box Predictionscited by 4
  31. 2022(Im)possibility of Collective Intelligencecited by 4
  32. 2023FairDP: Certified Fairness with Differential Privacycited by 4
  33. 2022Exploring the Unfairness of DP-SGD Across Settingscited by 3
  34. 2023Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testingcited by 3
  35. 2021Efficient Online-Bandit Strategies for Minimax Learning Problemscited by 2
  36. 2021The Sharpe predictor for fairness in machine learningcited by 2
  37. 2022Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivitycited by 2
  38. 2024Class-wise Generalization Error: an Information-Theoretic Analysiscited by 2

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