mlsanityData to 7 Oct 2026

Penalizing Unfairness in Binary Classification

arXiv 1707.00044 ↗First posted 30 Jun 2017cs.LG stat.MLcited by 25

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Abstract (arXiv)

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jun 2017.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 7; 2024: 2.

25 papers.

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  1. 2021Loss-Aversively Fair Classificationcited by 18
  2. 2022D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Biascited by 18
  3. 2021Removing biased data to improve fairness and accuracycited by 17
  4. 2022Subgroup Robustness Grows On Trees: An Empirical Baseline Investigationcited by 17
  5. 2022Survey on Fairness Notions and Related Tensionscited by 9
  6. 2023Learning Fair Classifiers via Min-Max F-divergence Regularizationcited by 8
  7. 2022Consistent Range Approximation for Fair Predictive Modelingcited by 7
  8. 2024Intrinsic Fairness-Accuracy Tradeoffs under Equalized Oddscited by 7
  9. 2022Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approachcited by 6
  10. 2022When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributescited by 6
  11. 2021Fairness-Driven Private Collaborative Machine Learningcited by 4
  12. 2023Holistic Survey of Privacy and Fairness in Machine Learningcited by 4
13 more
  1. 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
  2. 2024MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Searchcited by 4
  3. 2022SLIDE: a surrogate fairness constraint to ensure fairness consistencycited by 3
  4. 2021Fair Machine Learning under Limited Demographically Labeled Datacited by 2
  5. 2023Fair Causal Feature Selectioncited by 2
  6. 2022Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Datacited by 1
  7. 2023Within-group fairness: A guidance for more sound between-group fairnesscited by 1
  8. 2023Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendationscited by 1
  9. 2023Towards Fair and Explainable AI using a Human-Centered AI Approachcited by 1
  10. 2021Accounting for Model Uncertainty in Algorithmic Discriminationcited by 0
  11. 2021F3: Fair and Federated Face Attribute Classification with Heterogeneous Datacited by 0
  12. 2022On Disentangled and Locally Fair Representationscited by 0
  13. 2022Fair mappingcited by 0