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Machine learning is increasingly used to inform decision-making in sensitivesituations where decisions have consequential effects on individuals' lives. Inthese settings, in addition to requiring models to be accurate and robust,socially relevant values such as fairness, privacy, accountability, andexplainability play an important role for the adoption and impact of saidtechnologies. In this work, we focus on algorithmic recourse, which isconcerned with providing explanations and recommendations to individuals whoare unfavourably treated by automated decision-making systems. We first performan extensive literature review, and align the efforts of many authors bypresenting unified definitions, formulations, and solutions to recourse. Then,we provide an overview of the prospective research directions towards which thecommunity may engage, challenging existing assumptions and making explicitconnections to other ethical challenges such as security, privacy, andfairness.

A survey of algorithmic recourse: definitions, formulations, solutions, and prospects