竞争环境下的算法补救

中文摘要 AI 辅助

算法补救为那些从机器学习模型中获得不良结果的个体提供最低成本改进的建议，以实现期望的结果。计算补救措施时的一个核心假设是，在补救实施阶段决策规则保持不变。我们在个体竞争有限资源的场景中挑战这一假设。在这种场景下，即使用于评估个体的评分模型保持不变，广泛的补救实施也可能改变接受阈值。这种接受阈值的变化反过来可能使原有的补救建议失效（即遵循补救措施可能无法达到期望的结果）。为了解决这个问题，我们引入了一个名为“竞争下的补救”的框架，该框架联合优化了补救接收者以及他们需要满足的推荐分数目标，以在最初被拒绝的个体中平衡补救成本和转移后的有效性。我们开发了一种基于隐函数定理的算法，并对其性能进行了实证分析。在合成和真实数据集上的实验表明，个性化的分数目标可以实现更高的有效性，尽管成本更高。相比之下，常见的分数目标通常在较低到中等有效性值下提供有利的成本-有效性权衡。

英文摘要

Algorithmic recourse provides individuals who have received undesirable outcomes from machine learning models with suggestions for minimum-cost improvements to achieve the desired outcome. A central assumption when computing recourse is that the decision rule remains fixed throughout the recourse implementation phase. We challenge this assumption in settings where individuals compete for limited resources. In such settings, widespread recourse implementation can change the acceptance threshold even when the scoring model that is used to evaluate individuals remains the same. This change in acceptance threshold can, in turn, invalidate the original recourse recommendations (i.e., following the recourse may not lead to the desired outcome). To address this problem, we introduce a framework called recourse under competition that jointly optimizes for recommendation recipients and the recommended score target they need to satisfy to balance the recourse cost and post-shift validity among initially rejected individuals. We develop an algorithm based on the Implicit Function Theorem and empirically analyze its performance. Experiments on synthetic and real datasets show that personalized score targets can achieve higher validity, albeit at a higher cost. In contrast, common score targets generally offer favorable cost-validity trade-offs for lower to medium validity values.

发表机构

• Drexel University（德雷塞尔大学）

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