The Risks of Recourse in Binary Classification

arXiv:2306.00497v2 Announce Type: replace
Abstract: Algorithmic recourse provides explanations that help users overturn an unfavorable decision by a machine learning system. But so far very little attention has been paid to whether providing recourse is beneficial or not. We introduce an abstract learning-theoretic framework that compares the risks (i.e., expected losses) for classification with and without algorithmic recourse. This allows us to answer the question of when providing recourse is beneficial or harmful at the population level. Surprisingly, we find …

abstract arxiv attention binary classification cs.cy cs.lg decision framework losses machine machine learning risks stat.ml type