Multi-Agent Algorithmic Recourse

• URL: http://arxiv.org/abs/2110.00673v1
• Date: Fri, 1 Oct 2021 22:54:47 GMT
• Title: Multi-Agent Algorithmic Recourse
• Authors: Andrew O'Brien, Edward Kim
• Abstract summary: 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. We propose a new game theory inspired framework for providing algorithmic recourse in a multi-agent environment.
• Score: 7.23389716633927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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