mlsanityData to 7 Oct 2026

Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities

arXiv 2301.05753 ↗First posted 13 Jan 2023cs.CY cs.AI cs.LGcited by 5

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Abstract (arXiv)

As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated. However, various research communities have independently conceptualized these harms, envisioned potential applications, and proposed interventions. The result is a somewhat fractured landscape of literature focused generally on ensuring decision-making algorithms "do the right thing". In this paper, we compare and discuss work across two major subsets of this literature: algorithmic fairness, which focuses primarily on predictive systems, and ethical decision making, which focuses primarily on sequential decision making and planning. We explore how each of these settings has articulated its normative concerns, the viability of different techniques for these different settings, and how ideas from each setting may have utility for the other.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 98 of its 254 references that are in the map. First posted on arXiv 13 Jan 2023.

Builds on

98 of 254 references.

References · 98

  1. 2018A Reductions Approach to Fair Classificationcited by 585
  2. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  3. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
  4. 2018Actionable Recourse in Linear Classificationcited by 327
  5. 2019Measurement and Fairnesscited by 299
  6. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 225
  7. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  8. 2018Delayed Impact of Fair Machine Learningcited by 183
  9. 2021The Values Encoded in Machine Learning Researchcited by 172
  10. 2016Fairness in Learning: Classic and Contextual Banditscited by 167
  11. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
  12. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
38 more
  1. 2019Wasserstein Fair Classificationcited by 131
  2. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  3. 2020Safe Reinforcement Learning in Constrained Markov Decision Processescited by 109
  4. 2018The Social Cost of Strategic Classificationcited by 105
  5. 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
  6. 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
  7. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  8. 2018Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobservedcited by 94
  9. 2013Discrimination in Online Ad Deliverycited by 93
  10. 2022The Fallacy of AI Functionalitycited by 88
  11. 2022Partially Observable Markov Decision Processes in Robotics: A Surveycited by 81
  12. 2019Conditional Learning of Fair Representationscited by 78
  13. 2018Achieving Fairness through Adversarial Learning: an Application to Recidivism Predictioncited by 77
  14. 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 77
  15. 2018The Disparate Effects of Strategic Manipulationcited by 75
  16. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  17. 2019Combinatorial Sleeping Bandits with Fairness Constraintscited by 68
  18. 2020Fair Classification with Group-Dependent Label Noisecited by 67
  19. 2019Counterfactual Risk Assessments, Evaluation, and Fairnesscited by 59
  20. 2019Fair Decision Making using Privacy-Protected Datacited by 58
  21. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  22. 2020Fairness Through Robustness: Investigating Robustness Disparity in Deep Learningcited by 55
  23. 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
  24. 2019Algorithms for Fairness in Sequential Decision Makingcited by 53
  25. 2020Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionatelycited by 52
  26. 2021The Use and Misuse of Counterfactuals in Ethical Machine Learningcited by 52
  27. 2019FlipTest: Fairness Testing via Optimal Transportcited by 51
  28. 2019AdaFair: Cumulative Fairness Adaptive Boostingcited by 45
  29. 2017Calibrated Fairness in Banditscited by 42
  30. 2019The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationallycited by 41
  31. 2019Fair Contextual Multi-Armed Bandits: Theory and Experimentscited by 41
  32. 2019Fair Classification and Social Welfarecited by 40
  33. 2018Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Datacited by 39
  34. 2019What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systemscited by 38
  35. 2018From Fair Decision Making to Social Equalitycited by 36
  36. 2021Leave-one-out Unfairnesscited by 36
  37. 2020Fairness in Learning-Based Sequential Decision Algorithms: A Surveycited by 35
  38. 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33

50 most cited of 98 All 98 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.

  1. 2023Minimum Levels of Interpretability for Artificial Moral Agentscited by 5
  2. 2026Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learningcited by 2
  3. 2024Temporal Fairness in Decision Making Problemscited by 1
  4. 2024Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agentscited by 1
  5. 2024Fairness in Reinforcement Learning with Bisimulation Metricscited by 1