In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes, their proposal is only accompanied by a loose link to ideas from moral philosophy -- which makes it difficult to understand when the proposed criteria should be used to evaluate the fairness of a decision-making system. More recently, researchers have thus retroactively tried to tie existing fairness criteria to philosophical concepts. Group fairness criteria have typically been linked to egalitarianism, a theory of distributive justice. This makes it tempting to believe that fairness criteria mathematically represent ideals of distributive justice and this is indeed how they are typically portrayed. In this paper, we will discuss why the current approach of linking algorithmic fairness and distributive justice is too simplistic and, hence, insufficient. We argue that in the context of imperfect decision-making systems -- which is what we deal with in algorithmic fairness -- we should not only care about what the ideal distribution of benefits/harms among individuals would look like but also about how deviations from said ideal are distributed. Our claim is that algorithmic fairness is concerned with unfairness in these deviations. This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.

翻译：在算法公平领域，已有诸多公平准则被提出。这些准则的提出往往仅与道德哲学理念存在松散关联——这使得我们难以判断何时应采用特定准则来评估决策系统的公平性。近期，研究者开始尝试将现有公平准则与哲学概念进行回溯性关联。群体公平准则通常被关联至分配正义理论中的平等主义。这容易使人误认为公平准则在数学层面代表了分配正义的理想形态，而当前学界也确实普遍如此呈现。本文旨在论证：当前将算法公平与分配正义简单关联的研究路径过于简化且存在不足。我们认为，在不完美的决策系统（即算法公平研究所面对的现实场景）中，我们不仅应关注利益/损害在个体间的理想分配形态，更需关注偏离该理想状态的分布方式。本文主张：算法公平的核心关切正是这些偏离所蕴含的不公现象。这要求我们以算法公平研究者的身份，重新审视对待分配正义的视角及运用公平准则的方式。