Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
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作为机会均等的公平：政治哲学的规范指导
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Julia Stoyanovich
中科院分区：
文献类型：
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作者：
Falaah Arif Khan;Eleni Manis;Julia Stoyanovich
最近的兴趣编纂的公平性在自动决策系统（ADS），导致了广泛的配方是什么意思的算法系统是公平的。这些主张大多受到政治哲学学术的启发，但没有充分地扎根于政治哲学学术。本文旨在纠正这一缺陷。我们引入了一个公平理想的分类法，使用政治哲学中的机会平等（EOP）原则，澄清了它们在哲学中的概念，并提出了公平机器学习的编码。我们安排这些公平的理想上的EOP频谱，这是一个有用的框架，以指导设计一个公平的ADS在给定的上下文中。
我们使用我们的公平作为EOP框架，重新解释不可能的结果从哲学的角度来看，不同的价值体系之间的不兼容性，并展示了几个现实世界和假设的例子的框架的效用。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公平的含义，并为伦理学和法律的专家的类似奖学金铺平道路。
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.