论文标题
量表：从公平原则到限制决策
SCALES: From Fairness Principles to Constrained Decision-Making
论文作者
论文摘要
本文提出了秤，这是一个一般框架，将公平原则转化为基于约束马尔可夫决策过程（CMDP）的共同表示形式。借助因果语言，我们的框架可以对决策过程（程序公平）以及决策（结果公平）产生的结果构成限制。具体而言，我们表明可以将众所周知的公平原理编码为实用程序组件，非毒性组件或鳞片中心中的因果分量。我们使用涉及模拟医疗方案和现实世界中Compas数据集的一组案例研究来说明量表。实验表明，我们的框架产生了公平的政策，这些政策在单步和顺序决策方案中体现了替代性公平原则。
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.
