Incentives to Offer Algorithmic Recourse
This addresses the problem of ensuring fair and effective recourse in AI-driven high-stakes decisions for affected individuals, but it is incremental as it builds on existing XAI research.
The paper analyzes a decision-maker's incentives to offer algorithmic recourse to rejected applicants, finding that they only offer recourse to all applicants in extreme cases like when manipulation is impossible, and that some applicants may be worse off when recourse is offered.
Due to the importance of artificial intelligence (AI) in a variety of high-stakes decisions, such as loan approval, job hiring, and criminal bail, researchers in Explainable AI (XAI) have developed algorithms to provide users with recourse for an unfavorable outcome. We analyze the incentives for a decision-maker to offer recourse to a set of applicants. Does the decision-maker have the incentive to offer recourse to all rejected applicants? We show that the decision-maker only offers recourse to all applicants in extreme cases, such as when the recourse process is impossible to manipulate. Some applicants may be worse off when the decision-maker can offer recourse.