# Multi-Agent Algorithmic Recourse

arXiv: https://arxiv.org/abs/2110.00673 · HTML: https://mlsanity.com/paper/2110.00673/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020)
- [A survey of algorithmic recourse: definitions, formulations, solutions, and prospects](https://mlsanity.com/paper/2010.04050/) (2020)
- [Causal Structure Learning](https://mlsanity.com/paper/1706.09141/) (2017)
- [A Causal Perspective on Meaningful and Robust Algorithmic Recourse](https://mlsanity.com/paper/2107.07853/) (2021)

## Built on by (most cited first, 2 of 2)

- [Setting the Right Expectations: Algorithmic Recourse Over Time](https://mlsanity.com/paper/2309.06969/) (2023)
- [Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity](https://mlsanity.com/paper/2401.16088/) (2024)

## Abstract (arXiv)

The recent adoption of machine learning as a tool in real world decision making has spurred interest in understanding how these decisions are being made. Counterfactual Explanations are a popular interpretable machine learning technique that aims to understand how a machine learning model would behave if given alternative inputs. Many explanations attempt to go further and recommend actions an individual could take to obtain a more desirable output from the model. These recommendations are known as algorithmic recourse. Past work has largely focused on the effect algorithmic recourse has on a single agent. In this work, we show that when the assumption of a single agent environment is relaxed, current approaches to algorithmic recourse fail to guarantee certain ethically desirable properties. Instead, we propose a new game theory inspired framework for providing algorithmic recourse in a multi-agent environment that does guarantee these properties.