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 that there are many plausible scenarios in which providing recourse turns out to be harmful, because it pushes users to regions of higher class uncertainty and therefore leads to more mistakes. We further study whether the party deploying the classifier has an incentive to strategize in anticipation of having to provide recourse, and we find that sometimes they do, to the detriment of their users. Providing algorithmic recourse may therefore also be harmful at the systemic level. We confirm our theoretical findings in experiments on simulated and real-world data. All in all, we conclude that the current concept of algorithmic recourse is not reliably beneficial, and therefore requires rethinking.

翻译：算法追索权提供了帮助用户推翻机器学习系统不利决策的解释。但截至目前，很少有人关注提供追索权是否有益。我们提出一个抽象的学习理论框架，用于比较有算法追索权和无算法追索权分类的风险（即期望损失）。这使我们能够回答在总体层面上何时提供追索权有益或有害的问题。令人惊讶的是，我们发现存在许多合理情景，其中提供追索权反而有害，因为它将用户推向类别不确定性更高的区域，从而导致更多错误。我们进一步研究了部署分类器的一方是否因预期需要提供追索权而有策略性动机，并发现他们有时确实如此，这损害了用户的利益。因此，提供算法追索权在系统层面上也可能是有害的。我们通过模拟数据和真实世界数据的实验证实了理论发现。总之，我们得出结论：当前的算法追索权概念并不可靠地有益，因此需要重新审视。