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Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
arXiv - CS - Computers and Society Pub Date : 2021-06-15 , DOI: arxiv-2106.08259
Falaah Arif Khan, Eleni Manis, Julia Stoyanovich

Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithmic system to be fair. Most of these propositions are inspired by, but inadequately grounded in, political philosophy scholarship. This paper aims to correct that deficit. We introduce a taxonomy of fairness ideals using doctrines of Equality of Opportunity (EOP) from political philosophy, clarifying their conceptions in philosophy and the proposed codification in fair machine learning. We arrange these fairness ideals onto an EOP spectrum, which serves as a useful frame to guide the design of a fair ADS in a given context. We use our fairness-as-EOP framework to re-interpret the impossibility results from a philosophical perspective, as the in-compatibility between different value systems, and demonstrate the utility of the framework with several real-world and hypothetical examples. Through our EOP-framework we hope to answer what it means for an ADS to be fair from a moral and political philosophy standpoint, and to pave the way for similar scholarship from ethics and legal experts.

中文翻译:

作为机会均等的公平:来自政治哲学的规范指导

最近对自动决策系统 (ADS) 中的公平性进行编码的兴趣导致了对算法系统公平性意味着什么的广泛表述。这些命题中的大多数都受到政治哲学学术的启发,但没有足够的基础。本文旨在纠正这一缺陷。我们使用政治哲学中的机会平等 (EOP) 学说引入了公平理想的分类法,阐明了它们在哲学中的概念以及在公平机器学习中建议的编纂。我们将这些公平理想安排到 EOP 频谱上,该频谱可作为指导在给定上下文中设计公平 ADS 的有用框架。我们使用我们的公平作为 EOP 框架从哲学的角度重新解释不可能的结果,作为不同价值体系之间的不兼容,并通过几个真实世界和假设的例子来展示该框架的实用性。通过我们的 EOP 框架,我们希望从道德和政治哲学的角度回答 ADS 公平意味着什么,并为伦理和法律专家的类似研究铺平道路。
更新日期:2021-06-16
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