In prediction-based decision-making systems, different perspectives can be at odds: The short-term business goals of the decision makers are often in conflict with the decision subjects' wish to be treated fairly. Balancing these two perspectives is a question of values. However, these values are often hidden in the technicalities of the implementation of the decision-making system. In this paper, we propose a framework to make these value-laden choices clearly visible. We focus on a setting in which we want to find decision rules that balance the perspective of the decision maker and of the decision subjects. We provide an approach to formalize both perspectives, i.e., to assess the utility of the decision maker and the fairness towards the decision subjects. In both cases, the idea is to elicit values from decision makers and decision subjects that are then turned into something measurable. For the fairness evaluation, we build on well-known theories of distributive justice and on the algorithmic literature to ask what a fair distribution of utility (or welfare) looks like. This allows us to derive a fairness score that we then compare to the decision maker's utility. As we focus on a setting in which we are given a trained model and have to choose a decision rule, we use the concept of Pareto efficiency to compare decision rules. Our proposed framework can both guide the implementation of a decision-making system and help with audits, as it allows us to resurface the values implemented in a decision-making system.

翻译：在基于预测的决策系统中，不同视角可能相互冲突：决策者的短期商业目标往往与决策对象希望获得公平对待的诉求相矛盾。平衡这两个视角本质上是一个价值取向问题。然而，这些价值取向常常隐藏在决策系统实现的技术细节中。本文提出一个框架，旨在使这些蕴含价值的选择清晰可见。我们聚焦于需要制定决策规则以平衡决策者与决策对象双方视角的场景。我们提供了一种形式化表达双方视角的方法，即评估决策者的效用与对决策对象的公平性。在这两种情况下，核心理念是从决策者和决策对象处获取其价值偏好，并将其转化为可量化指标。在公平性评估方面，我们借鉴了成熟的分配正义理论及算法文献，探讨效用（或福祉）的公平分配应如何呈现。基于此，我们推导出公平性评分，并将其与决策者效用进行对比。由于我们关注的是在已有训练模型的情况下如何选择决策规则这一场景，我们采用帕累托效率概念来比较不同决策规则。该框架既能指导决策系统的实施，也可辅助审计工作——因为它能够重新呈现决策系统中所嵌入的价值取向。