Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions designed to ensure that individuals who are affected by the predictions of a machine learning model are treated fairly, typically measured in terms of a quantitative fairness metric. Despite the multitude of fairness metrics and fair-ml algorithms, there is still little guidance on the suitability of different approaches in practice. In this paper, we present a framework for moral reasoning about the justification of fairness metrics and explore the moral implications of the use of fair-ml algorithms that optimize for them. In particular, we argue that whether a distribution of outcomes is fair, depends not only on the cause of inequalities but also on what moral claims decision subjects have to receive a particular benefit or avoid a burden. We use our framework to analyze the suitability of two fairness metrics under different circumstances. Subsequently, we explore moral arguments that support or reject the use of the fair-ml algorithm introduced by Hardt et al. (2016). We argue that under very specific circumstances, particular metrics correspond to a fair distribution of burdens and benefits. However, we also illustrate that enforcing a fairness metric by means of a fair-ml algorithm may not result in the fair distribution of outcomes and can have several undesirable side effects. We end with a call for a more holistic evaluation of fair-ml algorithms, beyond their direct optimization objectives.

翻译：公平感知机器学习技术是一种算法干预手段，旨在确保受机器学习模型预测影响的个体得到公平对待，其公平性通常通过量化公平指标来衡量。尽管存在众多公平性指标和公平感知机器学习算法，但针对不同方法在实际应用中的适用性仍缺乏指导。本文提出一个用于道德推理公平性指标合理性的框架，并探讨优化这些指标的公平感知机器学习算法所蕴含的道德含义。具体而言，我们认为结果分布的公平性不仅取决于不平等的原因，还取决于决策对象在获得特定利益或避免负担方面具有何种道德主张。我们运用该框架分析两种公平性指标在不同情境下的适用性。随后，我们探讨支持或反对使用哈特等人（2016年）提出的公平感知机器学习算法的道德论据。我们认为，在非常特定的条件下，特定指标对应着负担与利益的公平分配。然而，我们也表明，通过公平感知机器学习算法强制执行某一公平性指标，可能并不会实现公平的结果分布，并且可能产生若干不良副作用。最后，我们呼吁对公平感知机器学习算法进行超越其直接优化目标的更全面评估。