Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We also consider fairness inquiries from a dynamic perspective, and further consider the long-term impact that is induced by current prediction and decision. In light of the differences in the characterized fairness, we present a flowchart that encompasses implicit assumptions and expected outcomes of different types of fairness inquiries on the data generating process, on the predicted outcome, and on the induced impact, respectively. This paper demonstrates the importance of matching the mission (which kind of fairness one would like to enforce) and the means (which spectrum of fairness analysis is of interest, what is the appropriate analyzing scheme) to fulfill the intended purpose.

翻译：算法公平性在机器学习领域受到了越来越多的关注。文献中提出了多种公平性的定义，但不同定义之间的差异与联系尚未得到清晰阐述。本文回顾并反思了机器学习文献中先前提出的各种公平性概念，并尝试将其与道德哲学及政治哲学中的论点（尤其是正义理论）建立关联。我们还从动态视角审视公平性问题，进一步考虑当前预测与决策所引发的长期影响。鉴于不同公平性表征之间的差异，我们提出一个流程图，分别涵盖不同类型公平性探究在数据生成过程、预测结果及诱导影响方面的隐式假设与预期结果。本文证明了匹配使命（希望强制实现哪种公平性）与手段（关注哪一谱系的公平性分析、适合的分析方案是什么）以实现预期目标的重要性。