Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making. The key idea is that one should not discriminate among individuals who would be similarly affected by the decision. Unlike the existing statistical definitions of fairness, principal fairness explicitly accounts for the fact that individuals can be impacted by the decision. We propose an axiomatic assumption that all groups are created equal. This assumption is motivated by a belief that protected attributes such as race and gender should have no direct causal effects on potential outcomes. Under this assumption, we show that principal fairness implies all three existing statistical fairness criteria once we account for relevant covariates. This result also highlights the essential role of conditioning covariates in resolving the previously recognized tradeoffs between the existing statistical fairness criteria. Finally, we discuss how to empirically choose conditioning covariates and then evaluate the principal fairness of a particular decision.

翻译：利用因果推断文献中的主要分层概念,我们为人类和算法决策引入了一个新的公平概念,称为主要公平;关键思想是,不应对受该决定同样影响的个人加以歧视;与现有的公平统计定义不同,主要公平明确说明个人可能受该决定影响的事实;我们提出一个所有群体平等创造的不言自明的假设;这一假设的动机是认为种族和性别等受保护属性不应对潜在结果产生直接的因果关系。根据这一假设,我们表明,一旦我们核算了相关的共变情况,则主要公平意味着所有三种现有的统计公平标准。这一结果还突出了调节共变因素在解决以前公认的现有统计公平标准之间的权衡方面的关键作用。最后,我们讨论如何以经验方式选择调节共变数,然后评价某项决定的主要公平性。