In this study, we critically examine the foundational premise of algorithmic recourse - a process of generating counterfactual action plans (i.e., recourses) assisting individuals to reverse adverse decisions made by AI systems. The assumption underlying algorithmic recourse is that individuals accept and act on recourses that minimize the gap between their current and desired states. This assumption, however, remains empirically unverified. To address this issue, we conducted a user study with 362 participants and assessed whether minimizing the distance function, a metric of the gap between the current and desired states, indeed prompts them to accept and act upon suggested recourses. Our findings reveal a nuanced landscape: participants' acceptance of recourses did not correlate with the recourse distance. Moreover, participants' willingness to act upon recourses peaked at the minimal recourse distance but was otherwise constant. These findings cast doubt on the prevailing assumption of algorithmic recourse research and signal the need to rethink the evaluation functions to pave the way for human-centered recourse generation.

翻译：本研究对算法性救济的基本前提进行了批判性审视——算法性救济是通过生成反事实行动计划（即救济方案）来帮助个体逆转人工智能系统不利决策的过程。该领域隐含的假设是：个体会接受并执行那些能最小化其当前状态与期望状态之间差距的救济方案。然而，这一假设迄今尚未得到实证验证。为解决此问题，我们开展了涉及362名参与者的用户研究，评估最小化距离函数（即衡量当前状态与期望状态间差距的指标）是否真能促使其接受并执行所建议的救济方案。我们的研究结果揭示了复杂图景：参与者对救济方案的接受程度与救济距离并无相关性。此外，参与者执行救济方案的意愿在最小救济距离处达到峰值，而在其他距离值上保持恒定。这些发现对当前算法性救济研究的主流假设提出了质疑，并表明需要重新思考评估函数，从而为以人为中心的救济生成开辟道路。