# From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse

arXiv: https://arxiv.org/abs/2205.15406 · HTML: https://mlsanity.com/paper/2205.15406/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2022.

## Built on by (most cited first, 6 of 6)

- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [Setting the Right Expectations: Algorithmic Recourse Over Time](https://mlsanity.com/paper/2309.06969/) (2023)
- [Achieving Diversity in Counterfactual Explanations: a Review and Discussion](https://mlsanity.com/paper/2305.05840/) (2023)
- [Learning Decision Trees and Forests with Algorithmic Recourse](https://mlsanity.com/paper/2406.01098/) (2024)
- [SIDEs: Separating Idealization from Deceptive Explanations in xAI](https://mlsanity.com/paper/2404.16534/) (2024)
- [Position: Explainable AI is Causality in Disguise](https://mlsanity.com/paper/2603.28597/) (2026)

## 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.