mlsanityData to 14 Oct 2026

Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity

arXiv 2401.16088 ↗First posted 29 Jan 2024cs.LG cs.CYcited by 9

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Abstract (arXiv)

Algorithmic recourse -- providing recommendations to those affected negatively by the outcome of an algorithmic system on how they can take action and change that outcome -- has gained attention as a means of giving persons agency in their interactions with artificial intelligence (AI) systems. Recent work has shown that even if an AI decision-making classifier is ``fair'' (according to some reasonable criteria), recourse itself may be unfair due to differences in the initial circumstances of individuals, compounding disparities for marginalized populations and requiring them to exert more effort than others. There is a need to define more methods and metrics for evaluating fairness in recourse that span a range of normative views of the world, and specifically those that take into account time. Time is a critical element in recourse because the longer it takes an individual to act, the more the setting may change due to model or data drift. This paper seeks to close this research gap by proposing two notions of fairness in recourse that are in normative alignment with substantive equality of opportunity, and that consider time. The first considers the (often repeated) effort individuals exert per successful recourse event, and the second considers time per successful recourse event. Building upon an agent-based framework for simulating recourse, this paper demonstrates how much effort is needed to overcome disparities in initial circumstances. We then proposes an intervention to improve the fairness of recourse by rewarding effort, and compare it to existing strategies.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

23 of 42 references.

References · 23

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2014Certifying and removing disparate impactcited by 715
  3. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  4. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  5. 2018Actionable Recourse in Linear Classificationcited by 328
  6. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  7. 2021Towards Robust and Reliable Algorithmic Recoursecited by 102
  8. 2016Measuring Fairness in Ranked Outputscited by 81
  9. 2020On the Fairness of Causal Algorithmic Recoursecited by 70
  10. 2019Equalizing Recourse across Groupscited by 55
  11. 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
  12. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
11 more
  1. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 39
  2. 2023The Possibility of Fairness: Revisiting the Impossibility Theorem in Practicecited by 18
  3. 2020Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directionscited by 17
  4. 2023Generating robust counterfactual explanationscited by 13
  5. 2023Setting the Right Expectations: Algorithmic Recourse Over Timecited by 13
  6. 2023Robustness Implies Fairness in Causal Algorithmic Recoursecited by 12
  7. 2022Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrinescited by 10
  8. 2022"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanationscited by 9
  9. 2023Endogenous Macrodynamics in Algorithmic Recoursecited by 9
  10. 2021A Causal Perspective on Meaningful and Robust Algorithmic Recoursecited by 8
  11. 2021Multi-Agent Algorithmic Recoursecited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 4.

9 papers.

  1. 2024Learning-Augmented Robust Algorithmic Recoursecited by 4
  2. 2025Optimal Robust Recourse with Lp-Bounded Model Changecited by 3
  3. 2025Understanding Endogenous Data Drift in Adaptive Models with Recourse-Seeking Userscited by 1
  4. 2025Your Recourse, My Loss? Algorithmic Recourse under Shared Constraintscited by 1
  5. 2026From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recoursecited by 1
  6. 2024Analyzing Fairness of Computer Vision and Natural Language Processing Modelscited by 0
  7. 2026Counterfactual Training: Teaching Models Plausible and Actionable Explanationscited by 0
  8. 2026When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Modelscited by 0
  9. 2026Algorithmic Recourse Under Competitioncited by 0