mlsanityData to 7 Oct 2026

A Distributionally Robust Approach to Fair Classification

arXiv 2007.09530 ↗First posted 18 Jul 2020cs.LG stat.MLcited by 43

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Abstract (arXiv)

We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity. This model is equivalent to a tractable convex optimization problem if a Wasserstein ball centered at the empirical distribution on the training data is used to model distributional uncertainty and if a new convex unfairness measure is used to incentivize equalized opportunities. We demonstrate that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets. We also derive linear programming-based confidence bounds on the level of unfairness of any pre-trained classifier by leveraging techniques from optimal uncertainty quantification over Wasserstein balls.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 7; 2023: 10; 2024: 7; 2025: 5; 2026: 4.

43 papers.

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  1. 2021Fair Mixup: Fairness via Interpolationcited by 109
  2. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  3. 2021Sinkhorn Distributionally Robust Optimizationcited by 38
  4. 2022Understanding Instance-Level Impact of Fairness Constraintscited by 27
  5. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  6. 2024Supervised Algorithmic Fairness in Distribution Shifts: A Surveycited by 23
  7. 2023Re-weighting Based Group Fairness Regularization via Classwise Robust Optimizationcited by 20
  8. 2021Poisoning Attacks on Fair Machine Learningcited by 19
  9. 2021Fairness-Aware PAC Learning from Corrupted Datacited by 18
  10. 2021Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solutioncited by 13
  11. 2021Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curationcited by 13
  12. 2023Flow-based Distributionally Robust Optimizationcited by 13
31 more
  1. 2021Wasserstein Robust Classification with Fairness Constraintscited by 10
  2. 2021Robust Fairness-aware Learning Under Sample Selection Biascited by 9
  3. 2022Distributionally Robust Fair Principal Components via Geodesic Descentscited by 9
  4. 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learningcited by 8
  5. 2022Consistent Range Approximation for Fair Predictive Modelingcited by 7
  6. 2023Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneouslycited by 7
  7. 2022Metrizing Fairnesscited by 6
  8. 2023Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spacescited by 4
  9. 2023f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimizationcited by 4
  10. 2021Fair Regression under Sample Selection Biascited by 3
  11. 2023On the Fairness ROAD: Robust Optimization for Adversarial Debiasingcited by 3
  12. 2024Inducing Group Fairness in Prompt-Based Language Model Decisionscited by 3
  13. 2024Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Driftcited by 3
  14. 2022Distributionally Robust Data Joincited by 2
  15. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  16. 2023Certification of Distributional Individual Fairnesscited by 2
  17. 2025Wasserstein Distributionally Robust Regret Optimizationcited by 2
  18. 2023Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classificationcited by 1
  19. 2023Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Frameworkcited by 1
  20. 2023Learning Fair Policies for Multi-stage Selection Problems from Observational Datacited by 1
  21. 2024A Distributionally Robust Optimisation Approach to Fair Credit Scoringcited by 1
  22. 2024Fair Clustering: Critique, Caveats, and Future Directionscited by 1
  23. 2025Global Group Fairness in Federated Learning via Function Trackingcited by 1
  24. 2026Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspectivecited by 1
  25. 2024Wasserstein Distributionally Robust Multiclass Support Vector Machinecited by 0
  26. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  27. 2025From Fragile to Certified: Wasserstein Audits of Group Fairness Under Distribution Shiftcited by 0
  28. 2025Wasserstein projection distance for fairness testing of regression modelscited by 0
  29. 2026Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbationscited by 0
  30. 2026textttfindr: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressionscited by 0
  31. 2026Fairness in multi-class multi-group classification problems via contextial coherent risk measurescited by 0