Algorithmic systems are often called upon to assist in high-stakes decision making. In light of this, algorithmic recourse, the principle wherein individuals should be able to take action against an undesirable outcome made by an algorithmic system, is receiving growing attention. The bulk of the literature on algorithmic recourse to-date focuses primarily on how to provide recourse to a single individual, overlooking a critical element: the effects of a continuously changing context. Disregarding these effects on recourse is a significant oversight, since, in almost all cases, recourse consists of an individual making a first, unfavorable attempt, and then being given an opportunity to make one or several attempts at a later date - when the context might have changed. This can create false expectations, as initial recourse recommendations may become less reliable over time due to model drift and competition for access to the favorable outcome between individuals. In this work we propose an agent-based simulation framework for studying the effects of a continuously changing environment on algorithmic recourse. In particular, we identify two main effects that can alter the reliability of recourse for individuals represented by the agents: (1) competition with other agents acting upon recourse, and (2) competition with new agents entering the environment. Our findings highlight that only a small set of specific parameterizations result in algorithmic recourse that is reliable for agents over time. Consequently, we argue that substantial additional work is needed to understand recourse reliability over time, and to develop recourse methods that reward agents' effort.

翻译：算法系统常被用于辅助高风险决策。为此，算法补救——即个体应能针对算法系统产生的不良结果采取行动的原则——正受到越来越多的关注。迄今为止，关于算法补救的文献主要聚焦于如何为单个个体提供补救，而忽视了一个关键要素：持续变化的环境所产生的影响。忽视这些影响是重大失误，因为在几乎所有情况下，补救过程都包含个体先进行首次不利尝试，随后在稍晚时间（此时环境可能已发生变化）获得一次或多次尝试机会。这会催生错误预期，因为初始补救建议可能因模型漂移及个体间对有利结果的竞争而随时间推移变得不再可靠。在本工作中，我们提出了一种基于智能体的仿真框架，用于研究持续变化环境对算法补救的影响。具体而言，我们识别出两种可能改变个体（由智能体代表）补救可靠性的主要效应：（1）与采取补救行动的其他智能体之间的竞争，以及（2）与新进入环境的智能体之间的竞争。我们的研究结果强调，仅有一小部分特定参数化设置能够使算法补救随时间推移对智能体保持可靠。因此，我们认为需要开展大量额外工作，以理解补救随时间推移的可靠性，并开发能奖励智能体努力的补救方法。