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：算法追索通过提供解释，帮助用户推翻机器学习系统的不利决策。但迄今为止，关于提供追索权是否有利的研究甚少。我们引入了一个抽象的、基于学习理论的框架，用于比较有无算法追索的分类风险（即期望损失）。这使得我们能够在总体层面上回答追索权何时有益或有害的问题。令人惊讶的是，我们发现诸多合理场景中提供追索权实际上是有害的，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有动力预判提供追索权而采取策略，并发现有时他们确实会这样做，从而损害用户利益。因此，提供算法追索在系统层面上也可能是有害的。我们在模拟数据和真实世界数据的实验中证实了这些理论发现。总之，我们得出结论：当前算法追索的概念并非可靠有益，因此需要重新审视。