从通用到个性化可执行性：重新审视算法救济中的个性化
From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse
AI总结：
本文重新审视算法救济中的个性化，提出将个性化定义为个体可执行性，通过硬约束和软约束分析其对救济关键指标的影响，揭示个性化对可执行性、成本和合理性的影响及潜在偏见。
摘要：
算法救济旨在提供可操作的建议，使个人能够改变不利的模型结果，先前的研究已广泛研究了效率、鲁棒性和公平性等属性。然而，救济中的个性化作用仍 largely 为人所隐含和探索不足。尽管现有方法通过用户交互整合了个性化元素，但通常缺乏对个性化显式定义，并未系统分析其对其他救济需求的下游影响。本文将个性化正式定义为个体可执行性，沿两个维度进行刻画：硬约束指明哪些特征是可操作的，软的个性化约束捕捉对行动值和成本的偏好。我们将在因果算法救济框架内操作化这些维度，采用预设用户提示方法，在生成任何救济建议之前，个人通过排名或评分表达偏好。通过广泛的实证评估，我们探讨了个性化如何与关键救济需求相互作用，包括有效性、成本和合理性。我们的结果突显了重要的权衡：个体可执行性约束，特别是硬约束，可以显著降低救济建议在摊销和非摊销方法中的合理性和有效性。值得注意的是，我们还发现，整合个体可执行性可以揭示不同社会人口群体在救济行动成本和合理性方面的差异。这些发现强调了在算法救济中对个性化进行原则性定义、谨慎操作化和严格评估的必要性。
英文摘要：
Algorithmic recourse aims to provide actionable recommendations that enable individuals to change unfavorable model outcomes, and prior work has extensively studied properties such as efficiency, robustness, and fairness. However, the role of personalization in recourse remains largely implicit and underexplored. While existing approaches incorporate elements of personalization through user interactions, they typically lack an explicit definition of personalization and do not systematically analyze its downstream effects on other recourse desiderata. In this paper, we formalize personalization as individual actionability, characterized along two dimensions: hard constraints that specify which features are individually actionable, and soft, individualized constraints that capture preferences over action values and costs. We operationalize these dimensions within the causal algorithmic recourse framework, adopting a pre-hoc user-prompting approach in which individuals express preferences via rankings or scores prior to the generation of any recourse recommendation. Through extensive empirical evaluation, we investigate how personalization interacts with key recourse desiderata, including validity, cost, and plausibility. Our results highlight important trade-offs: individual actionability constraints, particularly hard ones, can substantially degrade the plausibility and validity of recourse recommendations across amortized and non-amortized approaches. Notably, we also find that incorporating individual actionability can reveal disparities in the cost and plausibility of recourse actions across socio-demographic groups. These findings underscore the need for principled definitions, careful operationalization, and rigorous evaluation of personalization in algorithmic recourse.
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