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.

论文讨论公正机器学习算法的义务，对比不同的公正度量标准及其在道德层面的合理性，分析 Hardt 等人提出的公正机器学习算法引起的道德问题。作者建议需要从综合角度评价公正机器学习算法的策略和效果。