Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or loan approvals, into binary classification tasks. However, these approaches overlook that such decisions are not inherently binary (e.g., approve or not approve bail or loan); they also involve non-binary treatment decisions (e.g., bail conditions or loan terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). In this paper, we argue that non-binary treatment decisions are integral to the decision process and controlled by decision-makers and, therefore, should be central to fairness analyses in algorithmic decision-making. We propose a causal framework that extends fairness analyses and explicitly distinguishes between decision-subjects' covariates and the treatment decisions. This specification allows decision-makers to use our framework to (i) measure treatment disparity and its downstream effects in historical data and, using counterfactual reasoning, (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Moreover, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.

翻译：算法决策系统的公平性研究通常将复杂的决策过程（例如保释或贷款审批）简化为二元分类任务。然而，这些方法忽略了此类决策本质上并非二元（例如批准或不批准保释或贷款）；它们还涉及非二元处理决策（例如保释条件或贷款条款），这些决策可能影响下游结果（例如贷款偿还或再犯）。在本文中，我们认为非二元处理决策是决策过程中不可或缺的组成部分，且由决策者控制，因此应成为算法决策公平性分析的核心。我们提出了一个因果框架，该框架扩展了公平性分析，并明确区分了决策对象的协变量与处理决策。这一规范使决策者能够利用我们的框架：（i）在历史数据中衡量处理差异及其下游影响，并（ii）通过反事实推理，在自动化决策时减轻过去不公平处理决策的影响。我们运用该框架对四个广泛使用的贷款审批数据集进行了实证分析，揭示了非二元处理决策中潜在的差异及其对结果的歧视性影响，强调了在公平性评估中纳入处理决策的必要性。此外，通过对处理决策进行干预，我们证明了该框架能有效缓解历史数据中的处理歧视，从而确保公平的风险评分估计和（非二元）决策过程，使所有利益相关者受益。