mlsanityData to 7 Oct 2026

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

arXiv 1906.05082 ↗First posted 12 Jun 2019math.ST stat.ML stat.THcited by 60

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Abstract (arXiv)

We study the problem of fair binary classification using the notion of Equal Opportunity. It requires the true positive rate to distribute equally across the sensitive groups. Within this setting we show that the fair optimal classifier is obtained by recalibrating the Bayes classifier by a group-dependent threshold. We provide a constructive expression for the threshold. This result motivates us to devise a plug-in classification procedure based on both unlabeled and labeled datasets. While the latter is used to learn the output conditional probability, the former is used for calibration. The overall procedure can be computed in polynomial time and it is shown to be statistically consistent both in terms of the classification error and fairness measure. Finally, we present numerical experiments which indicate that our method is often superior or competitive with the state-of-the-art methods on benchmark datasets.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 16; 2023: 10; 2024: 9; 2025: 12; 2026: 4.

60 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  3. 2021Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projectionscited by 52
  4. 2022Fair and Optimal Classification via Post-Processingcited by 50
  5. 2021Sample Selection for Fair and Robust Trainingcited by 48
  6. 2021Your fairness may vary: Pretrained language model fairness in toxic text classificationcited by 38
  7. 2022Fairness Reprogrammingcited by 33
  8. 2021Classification with abstention but without disparitiescited by 27
  9. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  10. 2022Bayes-Optimal Classifiers under Group Fairnesscited by 24
  11. 2022Transferring Fairness under Distribution Shifts via Fair Consistency Regularizationcited by 21
  12. 2021Teacher's pet: understanding and mitigating biases in distillationcited by 20
38 more
  1. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  2. 2022Fair Bayes-Optimal Classifiers Under Predictive Paritycited by 16
  3. 2022Beyond Adult and COMPAS: Fairness in Multi-Class Predictioncited by 16
  4. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  5. 2023Improving Fair Training under Correlation Shiftscited by 14
  6. 2023Fairness in Multi-Task Learning via Wasserstein Barycenterscited by 14
  7. 2022To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontiercited by 10
  8. 2022Fairness-aware Model-agnostic Positive and Unlabeled Learningcited by 10
  9. 2022FairGrad: Fairness Aware Gradient Descentcited by 8
  10. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  11. 2024Intrinsic Fairness-Accuracy Tradeoffs under Equalized Oddscited by 7
  12. 2022Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictorscited by 5
  13. 2022FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guaranteecited by 5
  14. 2023Adapting Fairness Interventions to Missing Valuescited by 5
  15. 2023Arbitrariness Lies Beyond the Fairness-Accuracy Frontiercited by 5
  16. 2023How Far Can Fairness Constraints Help Recover From Biased Data?cited by 5
  17. 2024A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problemscited by 5
  18. 2024Regression under demographic parity constraints via unlabeled post-processingcited by 5
  19. 2022Breaking Fair Binary Classification with Optimal Flipping Attackscited by 4
  20. 2022Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Talecited by 4
  21. 2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processingcited by 4
  22. 2022SLIDE: a surrogate fairness constraint to ensure fairness consistencycited by 3
  23. 2023Parametric Fairness with Statistical Guaranteescited by 2
  24. 2024FairRR: Pre-Processing for Group Fairness through Randomized Responsecited by 2
  25. 2024Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structurecited by 2
  26. 2024Multi-Output Distributional Fairness via Post-Processingcited by 2
  27. 2025The Cost of Local and Global Fairness in Federated Learningcited by 2
  28. 2025Meta Optimality for Demographic Parity Constrained Regression via Post-Processingcited by 2
  29. 2025Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisationcited by 2
  30. 2025On Optimal Steering to Achieve Exact Fairnesscited by 2
  31. 2023Within-group fairness: A guidance for more sound between-group fairnesscited by 1
  32. 2024On the Power of Randomization in Fair Classification and Representationcited by 1
  33. 2025LoGoFair: Post-Processing for Local and Global Fairness in Federated Learningcited by 1
  34. 2025Bayes-Optimal Fair Classification with Multiple Sensitive Featurescited by 1
  35. 2026Fairness Aware Reward Optimizationcited by 1
  36. 2021Statistical Guarantees for Fairness Aware Plug-In Algorithmscited by 0
  37. 2021Fair Active Learning: Solving the Labeling Problem in Insurancecited by 0
  38. 2022Multiple Attribute Fairness: Application to Fraud Detectioncited by 0

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