论文标题
《公平现场指南：社会和形式科学的观点》
The Fairness Field Guide: Perspectives from Social and Formal Sciences
论文作者
论文摘要
在过去的几年中，已经提出了许多不同的方法来衡量机器学习模型的公平性。但是，尽管出版物和实施量越来越多，但仍然存在严重缺乏文献，这解释了公平的机器学习与哲学，社会学和法律的社会科学的相互作用。我们希望通过在本领域指南中累积和阐述由社会和正式和正式学习和统计学）科学所产生的公平机器学习的思想和讨论来解决这个问题。具体而言，除了提供几种流行的统计和基于因果的公平机器学习方法的数学和算法背景外，我们还解释了支持它们的基本哲学和法律思想。此外，我们探讨了从社会学和哲学观点中对当前的公平机器学习方法的批评。我们希望本领域指南将帮助公平的机器学习从业者更好地了解他们的算法如何与重要的人文价值（例如公平）保持一致，以及我们如何作为一种领域，设计方法和指标更好地服务于被压迫和边缘化的人群。
Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (specifically machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistical and causal-based fair machine learning methods, we explain the underlying philosophical and legal thoughts that support them. Further, we explore several criticisms of the current approaches to fair machine learning from sociological and philosophical viewpoints. It is our hope that this field guide will help fair machine learning practitioners better understand how their algorithms align with important humanistic values (such as fairness) and how we can, as a field, design methods and metrics to better serve oppressed and marginalized populaces.
