The definition and implementation of fairness in automated decisions has been extensively studied by the research community. Yet, there hides fallacious reasoning, misleading assertions, and questionable practices at the foundations of the current fair machine learning paradigm. Those flaws are the result of a failure to understand that the trade-off between statistically accurate outcomes and group similar outcomes exists as independent, external constraint rather than as a subjective manifestation as has been commonly argued. First, we explain that there is only one conception of fairness present in the fair machine learning literature: group similarity of outcomes based on a sensitive attribute where the similarity benefits an underprivileged group. Second, we show that there is, in fact, a trade-off between statistically accurate outcomes and group similar outcomes in any data setting where group disparities exist, and that the trade-off presents an existential threat to the equitable, fair machine learning approach. Third, we introduce a proof-of-concept evaluation to aid researchers and designers in understanding the relationship between statistically accurate outcomes and group similar outcomes. Finally, suggestions for future work aimed at data scientists, legal scholars, and data ethicists that utilize the conceptual and experimental framework described throughout this article are provided.

翻译：自动化决策中公平性的定义与实施已受到研究界的广泛关注。然而，当前公平机器学习范式的根基中潜藏着谬误推理、误导性论断及可疑实践。这些缺陷源于未能认识到：统计准确结果与群体相似结果之间的权衡，是独立存在的外部约束，而非如普遍观点所认为的主观表现。首先，我们阐明公平机器学习文献中仅存在一种公平概念：基于敏感属性（其相似性有利于弱势群体）的群体结果相似性。其次，我们证明，在任何存在群体差异的数据场景中，统计准确结果与群体相似结果之间确实存在权衡，且这种权衡对公平机器学习方法的公平性构成了根本性威胁。第三，我们引入概念验证评估方法，帮助研究人员和设计者理解统计准确结果与群体相似结果之间的关系。最后，我们为数据科学家、法律学者及数据伦理学家提供了未来工作建议，旨在利用本文所述的概念与实验框架。