mlsanityData to 14 Oct 2026

Wasserstein Fair Classification

arXiv 1907.12059 ↗First posted 28 Jul 2019stat.ML cs.LGcited by 131

Not on the map. Why?

Abstract (arXiv)

We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictions from model outputs. We introduce different methods that enable hiding sensitive information at test time or have a simple and fast implementation. We show empirical performance against different fairness baselines on several benchmark fairness datasets.

Key facts. Cited by 131 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 15; 2022: 25; 2023: 36; 2024: 20; 2025: 27; 2026: 8.

131 papers.

Sort:

By area:

  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  3. 2023A Survey on Fairness in Large Language Modelscited by 95
  4. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  5. 2021Mitigating Political Bias in Language Models Through Reinforced Calibrationcited by 73
  6. 2022Fair and Optimal Classification via Post-Processingcited by 51
  7. 2021Your fairness may vary: Pretrained language model fairness in toxic text classificationcited by 39
  8. 2022Achieving Fairness at No Utility Cost via Data Reweighing with Influencecited by 38
  9. 2022Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimizationcited by 33
  10. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  11. 2022On Learning Fairness and Accuracy on Multiple Subgroupscited by 32
  12. 2022Invariant Causal Mechanisms through Distribution Matchingcited by 31
38 more
  1. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  2. 2022Meta Optimal Transportcited by 28
  3. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  4. 2022Fair learning with Wasserstein barycenters for non-decomposable performance measurescited by 24
  5. 2022Fair Representation Learning through Implicit Path Alignmentcited by 22
  6. 2023Re-weighting Based Group Fairness Regularization via Classwise Robust Optimizationcited by 20
  7. 2022Conformalized Fairness via Quantile Regressioncited by 19
  8. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  9. 2021Understanding and Mitigating Accuracy Disparity in Regressioncited by 17
  10. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  11. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  12. 2023Fairness in Multi-Task Learning via Wasserstein Barycenterscited by 14
  13. 2023Post-hoc Bias Scoring Is Optimal For Fair Classificationcited by 14
  14. 2021Towards Return Parity in Markov Decision Processescited by 13
  15. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  16. 2022Fair Data Representation for Machine Learning at the Pareto Frontiercited by 12
  17. 2022Optimal Transport of Classifiers to Fairnesscited by 12
  18. 2022Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothingcited by 11
  19. 2021Fairness with Continuous Optimal Transportcited by 10
  20. 2021Fair Tree Classifier using Strong Demographic Paritycited by 9
  21. 2024Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMscited by 9
  22. 2021An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settingscited by 8
  23. 2022Repairing Regressors for Fair Binary Classification at Any Decision Thresholdcited by 8
  24. 2023Scalable Infomin Learningcited by 8
  25. 2023Computing high-dimensional optimal transport by flow neural networkscited by 8
  26. 2023Fair Wasserstein Coresetscited by 8
  27. 2024OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transportcited by 8
  28. 2024ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methodscited by 8
  29. 2022Fairness Implications of Encoding Protected Categorical Attributescited by 7
  30. 2022Mitigating Health Disparities in EHR via Deconfoundercited by 7
  31. 2023Mitigating Discrimination in Insurance with Wasserstein Barycenterscited by 7
  32. 2025Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metricscited by 7
  33. 2022Obtaining Dyadic Fairness by Optimal Transportcited by 6
  34. 2022Metrizing Fairnesscited by 6
  35. 2023Mean Parity Fair Regression in RKHScited by 6
  36. 2024On the (In)Compatibility between Group Fairness and Individual Fairnesscited by 6
  37. 2025Robust ML Auditing using Prior Knowledgecited by 6
  38. 2021Unified Shapley Framework to Explain Prediction Driftcited by 5

50 most cited of 131 All 131 (JSON)