论统计平价的道德辩护
On the Moral Justification of Statistical Parity
摘要：
算法公平性的一个关键但经常被忽视的方面是，我们如何从道德角度证明强制执行某种公平度量是合理的。当提出公平性度量时，通常通过强调其数学属性来对其进行论证。很少有人解释衡量标准下的道德假设。我们在本文中的目的是考虑与独立性的统计公平标准（统计平价）相关的道德方面。为此，我们考虑以前的工作，其中讨论了两个世界观“你所看到的就是你得到的”（WYSIWYG）和“我们都是平等的”（WAE），并通过这样做提供了一些指导，澄清可能的假设在算法的设计。我们提出了这项工作的延伸，它以道德为中心。最自然的道德延伸是，当且仅当社会人口群体之间的预测特征（例如高中成绩和标准化考试成绩可以预测大学表现）的差异是由不公正的社会差距或测量错误造成的时，独立性才需要实现。通过两个反例，我们证明了这一扩展是不普遍正确的。这意味着是否应该使用独立性的问题不能通过只考虑预测特征差异的公正性来满意地回答。
英文摘要：
A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics are proposed, they are typically argued for by highlighting their mathematical properties. Rarely are the moral assumptions beneath the metric explained. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence (statistical parity). To this end, we consider previous work, which discusses the two worldviews "What You See Is What You Get" (WYSIWYG) and "We're All Equal" (WAE) and by doing so provides some guidance for clarifying the possible assumptions in the design of algorithms. We present an extension of this work, which centers on morality. The most natural moral extension is that independence needs to be fulfilled if and only if differences in predictive features (e.g. high school grades and standardized test scores are predictive of performance at university) between socio-demographic groups are caused by unjust social disparities or measurement errors. Through two counterexamples, we demonstrate that this extension is not universally true. This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
