mlsanityData to 7 Oct 2026

Delayed Impact of Fair Machine Learning

arXiv 1803.04383 ↗First posted 12 Mar 2018cs.LG stat.MLcited by 183

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Abstract (arXiv)

Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.

Key facts. Cited by 183 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 36; 2022: 38; 2023: 38; 2024: 26; 2025: 26; 2026: 19.

183 papers.

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  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  3. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  4. 2021Towards Unbiased and Accurate Deferral to Multiple Expertscited by 71
  5. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  6. 2023Performative Prediction: Past and Futurecited by 52
  7. 2022Evaluation Gaps in Machine Learning Practicecited by 48
  8. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  9. 2021An Examination of Fairness of AI Models for Deepfake Detectioncited by 34
  10. 2021Stateful Strategic Regressioncited by 32
  11. 2022Fairness Transferability Subject to Bounded Distribution Shiftcited by 28
  12. 2022Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Makingcited by 25
38 more
  1. 2023Long-Term Fairness with Unknown Dynamicscited by 25
  2. 2024Large Language Models as Recommender Systems: A Study of Popularity Biascited by 25
  3. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  4. 2021Fair Classification with Adversarial Perturbationscited by 23
  5. 2023Causal Reinforcement Learning: A Surveycited by 22
  6. 2021Strategic Rankingcited by 21
  7. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  8. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 20
  9. 2022De-biasing "bias" measurementcited by 20
  10. 2021Unintended Selection: Persistent Qualification Rate Disparities and Interventionscited by 18
  11. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 18
  12. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  13. 2022Algorithmic Fairness in Business Analytics: Directions for Research and Practicecited by 18
  14. 2023Runtime Monitoring of Dynamic Fairness Propertiescited by 17
  15. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  16. 2021Achieving Counterfactual Fairness for Causal Banditcited by 15
  17. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  18. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
  19. 2023Improved Bayes Risk Can Yield Reduced Social Welfare Under Competitioncited by 14
  20. 2022Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systemscited by 13
  21. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  22. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12
  23. 2021Towards Return Parity in Markov Decision Processescited by 12
  24. 2022The Long Arc of Fairness: Formalisations and Ethical Discoursecited by 12
  25. 2023A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systemscited by 12
  26. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 12
  27. 2024Non-linear Welfare-Aware Strategic Learningcited by 12
  28. 2021Learning to be Fair: A Consequentialist Approach to Equitable Decision-Makingcited by 11
  29. 2022Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settingscited by 11
  30. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
  31. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  32. 2022Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrinescited by 10
  33. 2025Robustness and Cybersecurity in the EU Artificial Intelligence Actcited by 10
  34. 2021Fairness with Continuous Optimal Transportcited by 9
  35. 2022Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulationcited by 9
  36. 2022Reinforcement Learning with Stepwise Fairness Constraintscited by 9
  37. 2024Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutionscited by 9
  38. 2021Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracingcited by 8

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