公平的定义如何?调查公众对公平的算法定义的态度

N. Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, D. Parkes, Y. Liu
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引用次数: 136

摘要

定义算法公平性的最佳方式是什么?虽然在计算机科学文献中提出了许多关于公平的定义,但对于一个特定的定义并没有明确的共识。在这项工作中,我们调查了普通人对这三种公平定义的看法。在两个在线实验中,我们测试了在贷款决策的背景下,人们认为哪些定义是最公平的,以及公平观念是否会随着敏感信息(即贷款申请人的种族)的增加而改变。总体而言,一种定义(校准公平)往往比其他定义更受青睐,结果也为平权行动原则提供了支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness
What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people's perceptions of three of these fairness definitions. Across two online experiments, we test which definitions people perceive to be the fairest in the context of loan decisions, and whether fairness perceptions change with the addition of sensitive information (i.e., race of the loan applicants). Overall, one definition (calibrated fairness) tends to be more pre- ferred than the others, and the results also provide support for the principle of affirmative action.
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