文章基本信息

标题：Inherent Trade-Offs in the Fair Determination of Risk Scores
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作者：Jon Kleinberg ; Sendhil Mullainathan ; Manish Raghavan 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2017
卷号：67
页码：43:1-43:23
DOI：10.4230/LIPIcs.ITCS.2017.43
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously. Moreover, even satisfying all three conditions approximately requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them.
关键词：algorithmic fairness; risk tools; calibration