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

By Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, Isabel Valera. 2020

190 citations recorded by the source corpus.

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions towards which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.

Subfields: Artificial Intelligence, Safety Research, AI Ethics, Fairness & Society

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Citations in the corpus

On the Fairness of Causal Algorithmic Recourse (2020, methodology): “We extend the probabilistic framework of Karimi et al. (2020c) to consider causal recourse in the presence of heterogeneous features, see Appendix B.2 for more details.”
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “While providing some insight (explanation) into the underlying black-box classifier, such counterfactual explanations do not directly translate into actionable recommendations to individuals for obtaining a more favourable prediction[22, 5]—a related task referred to as algorithmic recourse [54, 55,”
Mathematical optimization in classification and regression trees (2021, background): “See Karimi et al. (2020), Sokol and Flach (2019), Verma et al. (2020) for recent surveys on counterfactual explanations.”
Deep Neural Networks and Tabular Data: A Survey (2021, background): “For a more fine-grained overview over the literature on counterfactual explanations we refer the interested reader to the most recent surveys [189], [190].”