From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse
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Abstract (arXiv)
People are increasingly subject to algorithmic decisions, and it is generally agreed that end-users should be provided an explanation or rationale for these decisions. There are different purposes that explanations can have, such as increasing user trust in the system or allowing users to contest the decision. One specific purpose that is gaining more traction is algorithmic recourse. We first propose that recourse should be viewed as a recommendation problem, not an explanation problem. Then, we argue that the capability approach provides plausible and fruitful ethical standards for recourse. We illustrate by considering the case of diversity constraints on algorithmic recourse. Finally, we discuss the significance and implications of adopting the capability approach for algorithmic recourse research.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 May 2022.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 1.
6 papers.
- 2024Visibility into AI Agentscited by 79
- 2023Setting the Right Expectations: Algorithmic Recourse Over Timecited by 13
- 2023Achieving Diversity in Counterfactual Explanations: a Review and Discussioncited by 11
- 2024Learning Decision Trees and Forests with Algorithmic Recoursecited by 7
- 2024SIDEs: Separating Idealization from Deceptive Explanations in xAIcited by 2
- 2026Position: Explainable AI is Causality in Disguisecited by 0
- 2026Position: Explainable AI is Causality in Disguise30 Mar 2026
- 2024Learning Decision Trees and Forests with Algorithmic Recourse3 Jun 2024
- 2024SIDEs: Separating Idealization from Deceptive Explanations in xAI25 Apr 2024
- 2024Visibility into AI Agents23 Jan 2024
- 2023Setting the Right Expectations: Algorithmic Recourse Over Time13 Sep 2023
- 2023Achieving Diversity in Counterfactual Explanations: a Review and Discussion10 May 2023