A growing body of literature in fairness-aware machine learning (fairML) aims to mitigate machine learning (ML)-related unfairness in automated decision-making (ADM) by defining metrics that measure fairness of an ML model and by proposing methods to ensure that trained ML models achieve low scores on these metrics. However, the underlying concept of fairness, i.e., the question of what fairness is, is rarely discussed, leaving a significant gap between centuries of philosophical discussion and the recent adoption of the concept in the ML community. In this work, we try to bridge this gap by formalizing a consistent concept of fairness and by translating the philosophical considerations into a formal framework for the training and evaluation of ML models in ADM systems. We argue that fairness problems can arise even without the presence of protected attributes (PAs), and point out that fairness and predictive performance are not irreconcilable opposites, but that the latter is necessary to achieve the former. Furthermore, we argue why and how causal considerations are necessary when assessing fairness in the presence of PAs by proposing a fictitious, normatively desired (FiND) world in which PAs have no causal effects. In practice, this FiND world must be approximated by a warped world in which the causal effects of the PAs are removed from the real-world data. Finally, we achieve greater linguistic clarity in the discussion of fairML. We outline algorithms for practical applications and present illustrative experiments on COMPAS data.

翻译：随着公平感知机器学习（fairML）领域文献的不断增长，研究者们旨在通过定义衡量机器学习（ML）模型公平性的指标，并提出确保训练后的ML模型在这些指标上取得低分的方法，来缓解自动化决策（ADM）中与机器学习相关的不公平问题。然而，其背后的公平性概念——即“公平是什么”这一问题——却鲜有讨论，这导致了数个世纪的哲学探讨与近期ML界对该概念的采纳之间存在显著鸿沟。在本研究中，我们试图通过形式化一个一致的公平概念，并将哲学考量转化为ADM系统中ML模型训练与评估的形式化框架，以弥合这一鸿沟。我们认为，即使在没有受保护属性（PAs）的情况下，公平性问题也可能出现，并指出公平性与预测性能并非不可调和的对立面，相反，后者是实现前者的必要条件。此外，我们通过提出一个虚构的、规范上理想的（FiND）世界——在该世界中PAs不具有因果效应——论证了为何以及如何在存在PAs的情况下评估公平性时必须进行因果考量。在实践中，这个FiND世界必须通过一个扭曲世界来近似，在该扭曲世界中，PAs的因果效应已从现实世界数据中移除。最后，我们在fairML的讨论中实现了更高的语言清晰度。我们概述了实际应用的算法，并在COMPAS数据上进行了说明性实验。