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Conditional Learning of Fair Representations

arXiv 1910.07162 ↗First posted 16 Oct 2019cs.LG cs.AI stat.MLcited by 78

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Abstract (arXiv)

We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. We show how these two components contribute to ensuring accuracy parity and equalized false-positive and false-negative rates across groups without impacting demographic parity. Furthermore, we also demonstrate both in theory and on two real-world experiments that the proposed algorithm leads to a better utility-fairness trade-off on balanced datasets compared with existing algorithms on learning fair representations for classification.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2019.

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Citing papers per year: 2021: 12; 2022: 23; 2023: 17; 2024: 12; 2025: 13; 2026: 1.

78 papers.

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  1. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  2. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  3. 2021Mitigating Political Bias in Language Models Through Reinforced Calibrationcited by 73
  4. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 66
  5. 2022Fair and Optimal Classification via Post-Processingcited by 50
  6. 2021DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimationcited by 48
  7. 2022Achieving Fairness at No Utility Cost via Data Reweighing with Influencecited by 38
  8. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  9. 2021Balancing out Bias: Achieving Fairness Through Balanced Trainingcited by 34
  10. 2022On Learning Fairness and Accuracy on Multiple Subgroupscited by 31
  11. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  12. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
38 more
  1. 2022Optimising Equal Opportunity Fairness in Model Trainingcited by 22
  2. 2022Fair Representation Learning through Implicit Path Alignmentcited by 22
  3. 2022Transferring Fairness under Distribution Shifts via Fair Consistency Regularizationcited by 21
  4. 2022FARE: Provably Fair Representation Learning with Practical Certificatescited by 21
  5. 2021Latent Space Smoothing for Individually Fair Representationscited by 18
  6. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  7. 2023FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classificationcited by 18
  8. 2021Understanding and Mitigating Accuracy Disparity in Regressioncited by 17
  9. 2023Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairnesscited by 17
  10. 2021Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Taskscited by 16
  11. 2023Improving Fair Training under Correlation Shiftscited by 14
  12. 2022MMD-B-Fair: Learning Fair Representations with Statistical Testingcited by 13
  13. 2022FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributescited by 12
  14. 2023Learning to Generate Equitable Text in Dialogue from Biased Training Datacited by 12
  15. 2023Bias Propagation in Federated Learningcited by 12
  16. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  17. 2022Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learningcited by 11
  18. 2022Conditional Supervised Contrastive Learning for Fair Text Classificationcited by 11
  19. 2022Certifying Some Distributional Fairness with Subpopulation Decompositioncited by 11
  20. 2023Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPcited by 11
  21. 2022Attainability and Optimality: The Equalized Odds Fairness Revisitedcited by 10
  22. 2021Costs and Benefits of Fair Regressioncited by 8
  23. 2021Fairness in Missing Data Imputationcited by 8
  24. 2022Towards Equal Opportunity Fairness through Adversarial Learningcited by 8
  25. 2024Rethinking Fair Representation Learning for Performance-Sensitive Taskscited by 8
  26. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  27. 2024Intrinsic Fairness-Accuracy Tradeoffs under Equalized Oddscited by 7
  28. 2022On Learning and Testing of Counterfactual Fairness through Data Preprocessingcited by 6
  29. 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunitiescited by 5
  30. 2024Intersectional Unfairness Discoverycited by 5
  31. 2024Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generationcited by 4
  32. 2024COD: Learning Conditional Invariant Representation for Domain Adaptation Regressioncited by 4
  33. 2022Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speechcited by 3
  34. 2022Systematic Evaluation of Predictive Fairnesscited by 3
  35. 2023Towards Poisoning Fair Representationscited by 3
  36. 2023Procedural Fairness Through Decoupling Objectionable Data Generating Componentscited by 3
  37. 2024Accurate and Data-Efficient Toxicity Prediction when Annotators Disagreecited by 3
  38. 2023Fairness in AI and Its Long-Term Implications on Societycited by 2

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