When using machine learning to aid decision-making, it is critical to ensure that an algorithmic decision is fair and does not discriminate against specific individuals/groups, particularly those from underprivileged populations. Existing group fairness methods aim to ensure equal outcomes (such as loan approval rates) across groups delineated by protected variables like race or gender. However, in cases where systematic differences between groups play a significant role in outcomes, these methods may overlook the influence of non-protected variables that can systematically vary across groups. These confounding factors can affect fairness evaluations, making it challenging to assess whether disparities are due to discrimination or inherent differences. Therefore, we recommend a more refined and comprehensive fairness index that accounts for both the systematic differences within groups and the multifaceted, intertwined confounding effects. The proposed index evaluates fairness on counterparts (pairs of individuals who are similar with respect to the task of interest but from different groups), whose group identities cannot be distinguished algorithmically by exploring confounding factors. To identify counterparts, we developed a two-step matching method inspired by propensity score and metric learning. In addition, we introduced a counterpart-based statistical fairness index, called Counterpart Fairness (CFair), to assess the fairness of machine learning models. Empirical results on the MIMIC and COMPAS datasets indicate that standard group-based fairness metrics may not adequately inform about the degree of unfairness present in predictions, as revealed through CFair.

翻译：在利用机器学习辅助决策时，确保算法决策的公平性且不歧视特定个体/群体（尤其是弱势群体）至关重要。现有的群体公平性方法旨在确保按受保护变量（如种族或性别）划分的各群体获得平等的结果（例如贷款批准率）。然而，当群体间的系统性差异对结果产生显著影响时，这些方法可能会忽略非受保护变量（这些变量可能在不同群体间存在系统性差异）的影响。这些混杂因素会影响公平性评估，使得难以判断差异究竟源于歧视还是固有差异。因此，我们提出一种更精细、更全面的公平性指标，该指标同时考虑了群体内的系统性差异以及多层面、相互交织的混杂效应。所提出的指标通过评估对应个体（即在相关任务上表现相似但来自不同群体的个体对）的公平性来实现，这些对应个体的群体身份无法通过探索混杂因素在算法上加以区分。为识别对应个体，我们受倾向得分和度量学习的启发，开发了一种两步匹配方法。此外，我们引入了一种基于对应个体的统计公平性指标，称为对应公平性（CFair），用于评估机器学习模型的公平性。在MIMIC和COMPAS数据集上的实证结果表明，如CFair所揭示的，标准的基于群体的公平性度量可能无法充分反映预测中存在的不公平程度。