mlsanityData to 7 Oct 2026

FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee

arXiv 2211.15072 ↗First posted 28 Nov 2022stat.ML cs.LGcited by 5

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Abstract (arXiv)

Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, and fairness could be violated when there is a modest number of samples, which is often the case in practice. In this paper, we propose FaiREE, a fair classification algorithm that can satisfy group fairness constraints with finite-sample and distribution-free theoretical guarantees. FaiREE can be adapted to satisfy various group fairness notions (e.g., Equality of Opportunity, Equalized Odds, Demographic Parity, etc.) and achieve the optimal accuracy. These theoretical guarantees are further supported by experiments on both synthetic and real data. FaiREE is shown to have favorable performance over state-of-the-art algorithms.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

26 of 55 references.

References · 26

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  3. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  4. 2019Conformalized Quantile Regressioncited by 620
  5. 2018A Reductions Approach to Fair Classificationcited by 585
  6. 2017On Fairness and Calibrationcited by 406
  7. 2017Algorithmic decision making and the cost of fairnesscited by 402
  8. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  9. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  10. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
  11. 2015The Variational Fair Autoencodercited by 223
  12. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
14 more
  1. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  2. 2018The Frontiers of Fairness in Machine Learningcited by 139
  3. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  4. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  5. 2017On Formalizing Fairness in Prediction with Machine Learningcited by 65
  6. 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
  7. 2016Auditing Black-box Models for Indirect Influencecited by 41
  8. 2016A statistical framework for fair predictive algorithmscited by 37
  9. 2022Bayes-Optimal Classifiers under Group Fairnesscited by 24
  10. 2016Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC)cited by 18
  11. 2018Enhancing the Accuracy and Fairness of Human Decision Makingcited by 13
  12. 2020Fair for All: Best-effort Fairness Guarantees for Classificationcited by 8
  13. 2022Enforcing Delayed-Impact Fairness Guaranteescited by 7
  14. 2022Finite- and Large- Sample Inference for Model and Coefficients in High-dimensional Linear Regression with Repro Samplescited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 2.

5 papers.

  1. 2023Learning Fair Representations with High-Confidence Guaranteescited by 1
  2. 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
  3. 2024On Demographic Group Fairness Guarantees in Deep Learningcited by 0
  4. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  5. 2025Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inferencecited by 0