论文标题
偏见，差异和公平的因果基础
Causal foundations of bias, disparity and fairness
论文作者
论文摘要
对性别或种族偏见等偏见的研究是社会和行为科学中的重要话题。但是，文献并不总是清楚地定义概念。偏见的定义通常是模棱两可的或根本不提供。要精确研究偏见，重要的是要有一个明确的偏见概念。我们建议将偏见定义为不合理的直接因果效应。我们建议将差异密切相关的概念定义为包括偏见的直接或间接因果效应。我们提出的定义可用于以更严格和系统的方式研究偏见和差异。我们将对偏见和差异的定义与人工智能文献中介绍的各种公平标准进行比较。此外，我们讨论了我们的定义与歧视的关系。我们在两个案例研究中说明了我们对偏见和差异的定义，重点介绍了警察枪击事件中科学和种族偏见的性别偏见。我们提出的定义旨在更好地欣赏偏见和差异研究的因果关系。我们希望这还将促进对此类研究的政策含义的改进。
The study of biases, such as gender or racial biases, is an important topic in the social and behavioural sciences. However, the literature does not always clearly define the concept. Definitions of bias are often ambiguous or not provided at all. To study biases in a precise manner, it is important to have a well-defined concept of bias. We propose to define bias as a direct causal effect that is unjustified. We propose to define the closely related concept of disparity as a direct or indirect causal effect that includes a bias. Our proposed definitions can be used to study biases and disparities in a more rigorous and systematic way. We compare our definitions of bias and disparity with various criteria of fairness introduced in the artificial intelligence literature. In addition, we discuss how our definitions relate to discrimination. We illustrate our definitions of bias and disparity in two case studies, focusing on gender bias in science and racial bias in police shootings. Our proposed definitions aim to contribute to a better appreciation of the causal intricacies of studies of biases and disparities. We hope that this will also promote an improved understanding of the policy implications of such studies.
