In order to monitor and prevent bias in AI systems we can use a wide range of (statistical) fairness measures. However, it is mathematically impossible to optimize for all of these measures at the same time. In addition, optimizing a fairness measure often greatly reduces the accuracy of the system (Kozodoi et al, 2022). As a result, we need a substantive theory that informs us how to make these decisions and for what reasons. I show that by using Rawls' notion of justice as fairness, we can create a basis for navigating fairness measures and the accuracy trade-off. In particular, this leads to a principled choice focusing on both the most vulnerable groups and the type of fairness measure that has the biggest impact on that group. This also helps to close part of the gap between philosophical accounts of distributive justice and the fairness literature that has been observed (Kuppler et al, 2021) and to operationalise the value of fairness.

翻译：为了监控和预防人工智能系统中的偏见，我们可以采用一系列（统计学的）公平性指标。然而，从数学角度无法同时优化所有这些指标。此外，优化某项公平性指标往往会导致系统准确率大幅下降（Kozodoi等，2022）。因此，我们需要一套实质性的理论来指导如何做出这些决策及其依据。本文表明，通过运用罗尔斯（Rawls）关于"作为公平的正义"这一概念，我们可以为权衡公平性指标与准确率之间的关系建立理论基础。具体而言，这将导向一个原则性的选择：既要关注最脆弱的群体，也要聚焦对该群体影响最大的公平性指标类型。这还有助于弥合分配正义的哲学论述与现有公平性文献之间被观察到的部分鸿沟（Kuppler等，2021），并将公平价值落地为可操作方案。