The statistical fairness field guide: perspectives from social and formal sciences
The statistical fairness field guide: perspectives from social and formal sciences
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DOI：
10.1007/s43681-022-00183-3
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Alycia N. Carey;Xintao Wu
中科院分区：
文献类型：
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作者：
Alycia N. Carey;Xintao Wu
在过去的几年里，已经提出了多种方法来衡量机器学习模型的公平性。然而，尽管出版物和实现的数量不断增加，但仍然严重缺乏解释公平机器学习与哲学、社会学和法学等社会科学的相互作用的文献。我们希望通过积累和阐述本领域指南中社会和形式(即机器学习和统计)科学产生的关于公平机器学习的思想和讨论来解决这个问题。具体地说，除了给出几个流行的基于统计的公平机器学习指标在公平机器学习中使用的数学和算法背景外，我们还解释了支持它们的潜在哲学和法律思想。此外，我们从社会学、哲学和法律的角度探讨了对当前公平机器学习方法的几个批评。我们希望这份实地指南能够帮助机器学习从业者识别和纠正算法侵犯人权和价值观的案例。
Over the past several years, a multitude of 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 (i.e., machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistics-based fair machine learning metrics used in fair machine learning, we explain the underlying philosophical and legal thoughts that support them. Furthermore, we explore several criticisms of the current approaches to fair machine learning from sociological, philosophical, and legal viewpoints. It is our hope that this field guide helps machine learning practitioners identify and remediate cases where algorithms violate human rights and values.