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A Reductions Approach to Fair Classification

arXiv 1803.02453 ↗First posted 6 Mar 2018cs.LGcited by 585

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Abstract (arXiv)

We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.

Key facts. Cited by 585 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 110; 2022: 137; 2023: 112; 2024: 80; 2025: 83; 2026: 63.

585 papers.

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  1. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  2. 2021Just Train Twice: Improving Group Robustness without Training Group Informationcited by 522
  3. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  4. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  5. 2022Last Layer Re-Training is Sufficient for Robustness to Spurious Correlationscited by 379
  6. 2021A survey on datasets for fairness-aware machine learningcited by 157
  7. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  8. 2022On Feature Learning in the Presence of Spurious Correlationscited by 145
  9. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  10. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  11. 2022Jury Learning: Integrating Dissenting Voices into Machine Learning Modelscited by 115
  12. 2021Fair Mixup: Fairness via Interpolationcited by 109
38 more
  1. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  2. 2021Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Searchcited by 90
  3. 2021User-oriented Fairness in Recommendationcited by 75
  4. 2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
  5. 2024FairCLIP: Harnessing Fairness in Vision-Language Learningcited by 69
  6. 2021Characterizing Fairness Over the Set of Good Models Under Selective Labelscited by 65
  7. 2021Fair Feature Distillation for Visual Recognitioncited by 59
  8. 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
  9. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  10. 2021Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hatecited by 50
  11. 2021Sample Selection for Fair and Robust Trainingcited by 48
  12. 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
  13. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  14. 2021Fairness of Exposure in Stochastic Banditscited by 42
  15. 2021Constrained Learning with Non-Convex Lossescited by 42
  16. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  17. 2023A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challengescited by 41
  18. 2021Learning Fair Classifiers with Partially Annotated Group Labelscited by 40
  19. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  20. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  21. 2022Achieving Fairness at No Utility Cost via Data Reweighing with Influencecited by 38
  22. 2021Disembodied Machine Learning: On the Illusion of Objectivity in NLPcited by 37
  23. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  24. 2024CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?cited by 35
  25. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  26. 2021Selective Regression Under Fairness Criteriacited by 34
  27. 2022Fairness Reprogrammingcited by 33
  28. 2021Stateful Strategic Regressioncited by 32
  29. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  30. 2023Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalizationcited by 31
  31. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  32. 2022Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regressioncited by 30
  33. 2022Fairness-aware Configuration of Machine Learning Librariescited by 28
  34. 2021Classification with abstention but without disparitiescited by 27
  35. 2022Active Fairness Auditingcited by 27
  36. 2022Understanding Instance-Level Impact of Fairness Constraintscited by 27
  37. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  38. 2021Simple and near-optimal algorithms for hidden stratification and multi-group learningcited by 26

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