mlsanityData to 7 Oct 2026

Bayes-Optimal Classifiers under Group Fairness

arXiv 2202.09724 ↗First posted 20 Feb 2022stat.ML cs.LGcited by 24

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Abstract (arXiv)

Machine learning algorithms are becoming integrated into more and more high-stakes decision-making processes, such as in social welfare issues. Due to the need of mitigating the potentially disparate impacts from algorithmic predictions, many approaches have been proposed in the emerging area of fair machine learning. However, the fundamental problem of characterizing Bayes-optimal classifiers under various group fairness constraints has only been investigated in some special cases. Based on the classical Neyman-Pearson argument (Neyman and Pearson, 1933; Shao, 2003) for optimal hypothesis testing, this paper provides a unified framework for deriving Bayes-optimal classifiers under group fairness. This enables us to propose a group-based thresholding method we call FairBayes, that can directly control disparity, and achieve an essentially optimal fairness-accuracy tradeoff. These advantages are supported by thorough experiments.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 28 of its 54 references that are in the map. First posted on arXiv 20 Feb 2022.

Builds on

28 of 54 references.

References · 28

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2014Certifying and removing disparate impactcited by 715
  3. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  4. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  5. 2017On Fairness and Calibrationcited by 406
  6. 2017Algorithmic decision making and the cost of fairnesscited by 402
  7. 2020Fairness in Machine Learning: A Surveycited by 369
  8. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  9. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  10. 2019Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigationcited by 252
  11. 2015The Variational Fair Autoencodercited by 223
  12. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
16 more
  1. 2016Fairness in Learning: Classic and Contextual Banditscited by 167
  2. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  3. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  4. 2019On the Fairness of Disentangled Representationscited by 144
  5. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  6. 2018Achieving Fairness through Adversarial Learning: an Application to Recidivism Predictioncited by 77
  7. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  8. 2020Learning Certified Individually Fair Representationscited by 69
  9. 2016Satisfying Real-world Goals with Dataset Constraintscited by 68
  10. 2018iFair: Learning Individually Fair Data Representations for Algorithmic Decision Makingcited by 64
  11. 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
  12. 2016A statistical framework for fair predictive algorithmscited by 37
  13. 2021Classification with abstention but without disparitiescited by 27
  14. 2019Improved Adversarial Learning for Fair Classificationcited by 25
  15. 2018Enhancing the Accuracy and Fairness of Human Decision Makingcited by 13
  16. 2020Fair Classification via Unconstrained Optimizationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 6; 2024: 9; 2025: 1; 2026: 3.

24 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2022Fair and Optimal Classification via Post-Processingcited by 50
  3. 2022Fair learning with Wasserstein barycenters for non-decomposable performance measurescited by 24
  4. 2024A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Modelscited by 20
  5. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  6. 2022Fair Bayes-Optimal Classifiers Under Predictive Paritycited by 16
  7. 2023Post-hoc Bias Scoring Is Optimal For Fair Classificationcited by 14
  8. 2024Intrinsic Fairness-Accuracy Tradeoffs under Equalized Oddscited by 7
  9. 2022FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guaranteecited by 5
  10. 2023How Far Can Fairness Constraints Help Recover From Biased Data?cited by 5
  11. 2024A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problemscited by 5
  12. 2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processingcited by 4
12 more
  1. 2023Mitigating Source Bias for Fairer Weak Supervisioncited by 3
  2. 2024Distribution-Free Rates in Neyman-Pearson Classificationcited by 3
  3. 2023Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributescited by 2
  4. 2024Multi-Output Distributional Fairness via Post-Processingcited by 2
  5. 2024Demographic parity in regression and classification within the unawareness frameworkcited by 2
  6. 2023On Comparing Fair Classifiers under Data Biascited by 1
  7. 2024On the Power of Randomization in Fair Classification and Representationcited by 1
  8. 2026Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraintscited by 1
  9. 2026Fairness Aware Reward Optimizationcited by 1
  10. 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
  11. 2025Toward Unifying Group Fairness Evaluation from a Sparsity Perspectivecited by 0
  12. 2026Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-offcited by 0