mlsanityData to 7 Oct 2026

Inherent Trade-Offs in the Fair Determination of Risk Scores

arXiv 1609.05807 ↗First posted 19 Sep 2016cs.LG cs.CY stat.MLcited by 582

Not on the map. Why?

Abstract (arXiv)

Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously. Moreover, even satisfying all three conditions approximately requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them.

Key facts. Cited by 582 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 107; 2022: 124; 2023: 105; 2024: 96; 2025: 72; 2026: 77.

582 papers.

Sort:

By area:

  1. 2020Aligning AI With Shared Human Valuescited by 706
  2. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  3. 2022Last Layer Re-Training is Sufficient for Robustness to Spurious Correlationscited by 379
  4. 2021Trustworthy AI: From Principles to Practicescited by 206
  5. 2022On Feature Learning in the Presence of Spurious Correlationscited by 145
  6. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  7. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  8. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  9. 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systemscited by 94
  10. 2021A Survey on Bias in Visual Datasetscited by 84
  11. 2021Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibrationcited by 79
  12. 2021Label-Imbalanced and Group-Sensitive Classification under Overparameterizationcited by 74
38 more
  1. 2021Fair Conformal Predictors for Applications in Medical Imagingcited by 74
  2. 2021Omnipredictorscited by 71
  3. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 66
  4. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  5. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  6. 2021Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarkingcited by 53
  7. 2022Improving the Fairness of Chest X-ray Classifierscited by 52
  8. 2022A Unifying Theory of Distance from Calibrationcited by 51
  9. 2022Fair and Optimal Classification via Post-Processingcited by 50
  10. 2021Online Multivalid Learning: Means, Moments, and Prediction Intervalscited by 48
  11. 2021Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffscited by 48
  12. 2021Top-label calibration and multiclass-to-binary reductionscited by 46
  13. 2021Controllable Guarantees for Fair Outcomes via Contrastive Information Estimationcited by 45
  14. 2022Low-Degree Multicalibrationcited by 44
  15. 2021Deep Cox Mixtures for Survival Regressioncited by 43
  16. 2021Fairness of Exposure in Stochastic Banditscited by 42
  17. 2021Multi-group Agnostic PAC Learnabilitycited by 42
  18. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  19. 2021FARF: A Fair and Adaptive Random Forests Classifiercited by 37
  20. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  21. 2024CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?cited by 35
  22. 2021Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Researchcited by 34
  23. 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
  24. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  25. 2021Stateful Strategic Regressioncited by 32
  26. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  27. 2022Beyond calibration: estimating the grouping loss of modern neural networkscited by 32
  28. 2023Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparitiescited by 31
  29. 2023Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classificationcited by 31
  30. 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibrationcited by 31
  31. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  32. 2022Bias Mimicking: A Simple Sampling Approach for Bias Mitigationcited by 30
  33. 2022Causal Proxy Models for Concept-Based Model Explanationscited by 29
  34. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  35. 2023On the Independence of Association Bias and Empirical Fairness in Language Modelscited by 29
  36. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  37. 2021Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literaturecited by 27
  38. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27

50 most cited of 582 All 582 (JSON)