This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (CMDP). With the help of causal language, our framework can place constraints on both the procedure of decision making (procedural fairness) as well as the outcomes resulting from decisions (outcome fairness). Specifically, we show that well-known fairness principles can be encoded either as a utility component, a non-causal component, or a causal component in a SCALES-CMDP. We illustrate SCALES using a set of case studies involving a simulated healthcare scenario and the real-world COMPAS dataset. Experiments demonstrate that our framework produces fair policies that embody alternative fairness principles in single-step and sequential decision-making scenarios.

本文提出了一个新的框架Scales，利用基于约束马尔科夫决策过程（CMDP）的公共表示将公平性原则转化为一种通用的框架。利用因果语言，该框架可以同时对决策过程（程序公平性）和决策结果（结果公平性）施加约束， 同时展示了Scales-cmdp中知名的公平性原则可以编码为一个效用组件、非因果组件或者因果组件。最后，作者用健康医疗模拟和真实的COMPAS数据集为例展示了SCALES。实验表明，这个框架在单步和序贯决策场景中可以制定体现多种公平性原则的公平政策。

SCALES: 从公平原则到受限决策制定