mlsanityData to 7 Oct 2026

Group-blind optimal transport to group parity and its constrained variants

arXiv 2310.11407 ↗First posted 17 Oct 2023cs.LG math.OCcited by 4

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Abstract (arXiv)

Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to addressing groups categorised by protected attributes, e.g., gender, race. Prevailing algorithms in fair learning predominantly hinge on accessibility or estimations of these protected attributes, at least in the training process. We design a single group-blind projection map that aligns the feature distributions of both groups in the source data, achieving (demographic) group parity, without requiring values of the protected attribute for individual samples in the computation of the map, as well as its use. Instead, our approach utilises the feature distributions of the privileged and unprivileged groups in a boarder population and the essential assumption that the source data are unbiased representation of the population. We present numerical results on synthetic data and real data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 54 of its 101 references that are in the map. First posted on arXiv 17 Oct 2023.

Builds on

54 of 101 references.

References · 54

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2017Counterfactual Fairnesscited by 802
  4. 2018Computational Optimal Transportcited by 765
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  7. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  8. 2017Joint Distribution Optimal Transportation for Domain Adaptationcited by 280
  9. 2020No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problemscited by 208
  10. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  11. 2020Fairness in Machine Learningcited by 194
  12. 2018Path-Specific Counterfactual Fairnesscited by 179
38 more
  1. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  2. 2019Inherent Tradeoffs in Learning Fair Representationscited by 144
  3. 2019Wasserstein Fair Classificationcited by 131
  4. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  5. 2020Fair Regression with Wasserstein Barycenterscited by 99
  6. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  7. 2016Fairness in Reinforcement Learningcited by 93
  8. 2020On the Privacy Risks of Algorithmic Fairnesscited by 82
  9. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  10. 2018Bias Mitigation Post-processing for Individual and Group Fairnesscited by 72
  11. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  12. 2020On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithmcited by 61
  13. 2021Post-processing for Individual Fairnesscited by 61
  14. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  15. 2019FlipTest: Fairness Testing via Optimal Transportcited by 51
  16. 2018Learning Optimal Fair Policiescited by 49
  17. 2022Causal Fairness Analysiscited by 42
  18. 2021Improving Mini-batch Optimal Transport via Partial Transportationcited by 40
  19. 2020Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approachcited by 39
  20. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  21. 2020Ensuring Fairness Beyond the Training Datacited by 35
  22. 2021Unbalanced Optimal Transport through Non-negative Penalized Linear Regressioncited by 31
  23. 2020Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transportcited by 28
  24. 2018Optimal Transport: Fast Probabilistic Approximation with Exact Solverscited by 27
  25. 2018Taking Advantage of Multitask Learning for Fair Classificationcited by 24
  26. 2021Testing Group Fairness via Optimal Transport Projectionscited by 23
  27. 2019Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularizationcited by 18
  28. 2022Fair Bayes-Optimal Classifiers Under Predictive Paritycited by 16
  29. 2015Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metriccited by 15
  30. 2022Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairnesscited by 13
  31. 2021A Unified Approach to Fair Online Learning via Blackwell Approachabilitycited by 12
  32. 2022Optimal Transport of Classifiers to Fairnesscited by 12
  33. 2019Optimal Training of Fair Predictive Modelscited by 10
  34. 2021Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approachcited by 9
  35. 2021On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexitycited by 8
  36. 2023Causal Fairness for Outcome Controlcited by 8
  37. 2022Obtaining Dyadic Fairness by Optimal Transportcited by 6
  38. 2022Individually Fair Learning with One-Sided Feedbackcited by 5

50 most cited of 54 All 54 (JSON)

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Cited by year

Citing papers per year: 2024: 4.

4 papers.

  1. 2024Optimal Transport for Fairness: Archival Data Repair using Small Research Data Setscited by 3
  2. 2024Demographic parity in regression and classification within the unawareness frameworkcited by 2
  3. 2024Fairness in Ranking: Robustness through Randomization without the Protected Attributecited by 1
  4. 2024Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographicscited by 0