论文标题
部分可观测时空混沌系统的无模型预测
Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics
论文作者
论文摘要
团体公平指标是评估基于预测决策系统公平性的既定方法。但是,这些指标仍然与哲学理论联系不足,而且它们的道德含义通常不清楚。在本文中,我们为团体公平指标提出了一个综合框架,将它们与更多的分配正义理论联系起来。不同的群体公平度量指标在如何衡量对受影响个人的决定的利益或损害的选择上有所不同,以及假定对利益的道德主张。我们的统一框架揭示了与标准群体公平指标相关的规范选择,并允许对其道德物质进行解释。此外,这种更广泛的观点为我们在文献中发现的标准公平指标的扩展提供了结构。这种扩展允许解决对标准群体公平指标的几种批评,特别是:(1)它们是基于平等的,即,他们要求群体之间的某种形式的平等,有时可能对边缘化的群体有害; (2)他们仅比较跨小组的决定,但对这些群体的后果没有比较; (3)分配正义文献的全部广度不足。
Group fairness metrics are an established way of assessing the fairness of prediction-based decision-making systems. However, these metrics are still insufficiently linked to philosophical theories, and their moral meaning is often unclear. In this paper, we propose a comprehensive framework for group fairness metrics, which links them to more theories of distributive justice. The different group fairness metrics differ in their choices about how to measure the benefit or harm of a decision for the affected individuals, and what moral claims to benefits are assumed. Our unifying framework reveals the normative choices associated with standard group fairness metrics and allows an interpretation of their moral substance. In addition, this broader view provides a structure for the expansion of standard fairness metrics that we find in the literature. This expansion allows addressing several criticisms of standard group fairness metrics, specifically: (1) they are parity-based, i.e., they demand some form of equality between groups, which may sometimes be detrimental to marginalized groups; (2) they only compare decisions across groups but not the resulting consequences for these groups; and (3) the full breadth of the distributive justice literature is not sufficiently represented.