Ensuring fairness of prediction-based decision making is based on statistical group fairness criteria. Which one of these criteria is the morally most appropriate one depends on the context, and its choice requires an ethical analysis. In this paper, we present a step-by-step procedure integrating three elements: (a) a framework for the moral assessment of what fairness means in a given context, based on the recently proposed general principle of "Fair equality of chances" (FEC) (b) a mapping of the assessment's results to established statistical group fairness criteria, and (c) a method for integrating the thus-defined fairness into optimal decision making. As a second contribution, we show new applications of the FEC principle and show that, with this extension, the FEC framework covers all types of group fairness criteria: independence, separation, and sufficiency. Third, we introduce an extended version of the FEC principle, which additionally allows accounting for morally irrelevant elements of the fairness assessment and links to well-known relaxations of the fairness criteria. This paper presents a framework to develop fair decision systems in a conceptually sound way, combining the moral and the computational elements of fair prediction-based decision-making in an integrated approach. Data and code to reproduce our results are available at https://github.com/joebaumann/fair-prediction-based-decision-making.
翻译:确保基于预测的决策的公平性以统计群体公平性标准为基础。这些标准之一在道德上最适当,取决于背景,其选择需要道德分析。本文提出一个渐进式程序,包括三个要素:(a) 根据最近提出的“公平机会平等”一般原则(FEC)(b) 将评估结果与既定统计群体公平性标准挂钩,(c) 将评估结果与既定统计群体公平性标准挂钩,以及(c) 将由此定义的公平性纳入最佳决策的方法。作为第二项贡献,我们展示了FEC原则的新应用情况,并表明,在这一扩展之后,FEC框架涵盖所有类型的群体公平标准:独立、分离和充分性。第三,我们引入了FEC原则的扩展版本,允许对公平性评估中道德上无关的要素进行会计,并与众所周知的公平性标准的放松挂钩。本文提出了一个框架,以概念上稳妥的方式发展公平的决策系统,将基于公平预测/决定的道德和计算要素结合起来。在基于AD/IB/MA/决定性综合法中将基于道德和计算结果。