Algorithmic recourse -- providing recommendations to those affected negatively by the outcome of an algorithmic system on how they can take action and change that outcome -- has gained attention as a means of giving persons agency in their interactions with artificial intelligence (AI) systems. Recent work has shown that even if an AI decision-making classifier is ``fair'' (according to some reasonable criteria), recourse itself may be unfair due to differences in the initial circumstances of individuals, compounding disparities for marginalized populations and requiring them to exert more effort than others. There is a need to define more methods and metrics for evaluating fairness in recourse that span a range of normative views of the world, and specifically those that take into account time. Time is a critical element in recourse because the longer it takes an individual to act, the more the setting may change due to model or data drift. This paper seeks to close this research gap by proposing two notions of fairness in recourse that are in normative alignment with substantive equality of opportunity, and that consider time. The first considers the (often repeated) effort individuals exert per successful recourse event, and the second considers time per successful recourse event. Building upon an agent-based framework for simulating recourse, this paper demonstrates how much effort is needed to overcome disparities in initial circumstances. We then proposes an intervention to improve the fairness of recourse by rewarding effort, and compare it to existing strategies.

翻译：算法追责——即向受算法系统负面结果影响的个体提供行动建议，助其改变该结果——作为增强人与人工智能(AI)系统互动中自主性的一种手段，已引起广泛关注。但近期研究表明，即便AI决策分类器符合某些合理的公平标准，追责本身可能因个体初始境况差异而产生不公平，加剧边缘化群体面临的差距，并迫使其付出比他人更多的努力。因此，亟需定义更多评估追责公平性的方法与指标，这些方法应涵盖多种规范性世界观，尤其是考虑时间维度。时间在追责中至关重要，因为个体采取行动所需时间越长，模型或数据漂移可能带来的环境变化就越大。本文旨在填补这一研究空白，提出两种与实质性机会平等原则相符且考虑时间因素的追责公平性概念：第一种关注个体每次成功追责所付出的（通常重复性的）努力，第二种则关注每次成功追责所需的时间。基于代理建模的追责模拟框架，本文论证了克服初始境况差距所需付出的努力程度，并进一步提出一种通过奖励努力来改善追责公平性的干预措施，同时将其与现有策略进行比较。