Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
It addresses fairness issues in algorithmic recourse for individuals affected by automated decisions, offering a novel theoretical and practical approach.
The paper tackles unfairness in algorithmic recourse by introducing a fairness framework based on social burden and a practical algorithm (MISOB), which reduces social burden across all groups without compromising classifier accuracy in real-world datasets.
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring the fairness of classifiers. Beyond just fair classification, emerging legislation now mandates that when a classifier delivers a negative decision, it must also offer actionable steps an individual can take to reverse that outcome. This concept is known as algorithmic recourse. Nevertheless, many researchers have expressed concerns about the fairness guarantees within the recourse process itself. In this work, we provide a holistic theoretical characterization of unfairness in algorithmic recourse, formally linking fairness guarantees in recourse and classification, and highlighting limitations of the standard equal cost paradigm. We then introduce a novel fairness framework based on social burden, along with a practical algorithm (MISOB), broadly applicable under real-world conditions. Empirical results on real-world datasets show that MISOB reduces the social burden across all groups without compromising overall classifier accuracy.