mlsanityData to 14 Oct 2026

Post-hoc Bias Scoring Is Optimal For Fair Classification

arXiv 2310.05725 ↗First posted 9 Oct 2023stat.ML cs.LGcited by 14

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Abstract (arXiv)

We consider a binary classification problem under group fairness constraints, which can be one of Demographic Parity (DP), Equalized Opportunity (EOp), or Equalized Odds (EO). We propose an explicit characterization of Bayes optimal classifier under the fairness constraints, which turns out to be a simple modification rule of the unconstrained classifier. Namely, we introduce a novel instance-level measure of bias, which we call bias score, and the modification rule is a simple linear rule on top of the finite amount of bias scores.Based on this characterization, we develop a post-hoc approach that allows us to adapt to fairness constraints while maintaining high accuracy. In the case of DP and EOp constraints, the modification rule is thresholding a single bias score, while in the case of EO constraints we are required to fit a linear modification rule with 2 parameters. The method can also be applied for composite group-fairness criteria, such as ones involving several sensitive attributes.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 9 Oct 2023.

Builds on

24 of 40 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  4. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  6. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  7. 2018A Reductions Approach to Fair Classificationcited by 587
  8. 2015Train faster, generalize better: Stability of stochastic gradient descentcited by 552
  9. 2020Fairness in Machine Learning: A Surveycited by 370
  10. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  11. 2019Wasserstein Fair Classificationcited by 131
  12. 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
12 more
  1. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  2. 2022Fair Contrastive Learning for Facial Attribute Classificationcited by 71
  3. 2020Fair Classification with Group-Dependent Label Noisecited by 67
  4. 2020Robust Fairness under Covariate Shiftcited by 58
  5. 2022Fair and Optimal Classification via Post-Processingcited by 51
  6. 2021Stability and Deviation Optimal Risk Bounds with Convergence Rate O(1/n)cited by 42
  7. 2020Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Networkcited by 38
  8. 2022Understanding Instance-Level Impact of Fairness Constraintscited by 27
  9. 2022Bayes-Optimal Classifiers under Group Fairnesscited by 24
  10. 2022Fair learning with Wasserstein barycenters for non-decomposable performance measurescited by 24
  11. 2021Group-Aware Threshold Adaptation for Fair Classificationcited by 21
  12. 2022Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributescited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 8; 2026: 3.

14 papers.

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  1. 2024Fairness Without Harm: An Influence-Guided Active Sampling Approachcited by 12
  2. 2024A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problemscited by 5
  3. 2025FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repaircited by 4
  4. 2024On the Maximal Local Disparity of Fairness-Aware Classifierscited by 3
  5. 2025Meta Optimality for Demographic Parity Constrained Regression via Post-Processingcited by 2
  6. 2025LoGoFair: Post-Processing for Local and Global Fairness in Federated Learningcited by 1
  7. 2025Bayes-Optimal Fair Classification with Multiple Sensitive Featurescited by 1
  8. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  9. 2025FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learningcited by 0
  10. 2025Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processingcited by 0
  11. 2025Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliancecited by 0
  12. 2026Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learningcited by 0
2 more
  1. 2026Demystifying the Optimal Fair Classifier in Multi-Class Classificationcited by 0
  2. 2026Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systemscited by 0