文章基本信息

标题：Data-Driven Discrimination at Work
本地全文：下载
作者：Kim, Pauline T.
期刊名称：William & Mary Law Review
印刷版ISSN：0043-5589
电子版ISSN：2374-8524
出版年度：2017
卷号：58
期号：3
页码：857
出版社：College of William & Mary Law School
摘要：A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired, or promoted. Although data algorithms can help to avoid biased human decision-making, they also risk introducing new sources of bias. Algorithms built on inaccurate, biased, or unrepresentative data can produce outcomes biased along lines of race, sex, or other protected characteristics. Data mining techniques may cause employment decisions to be based on correlations rather than causal relationships; they may obscure the basis on which employment decisions are made; and they may further exacerbate inequality because error detection is limited and feedback effects compound the bias. Given these risks, I argue for a legal response to classification bias—a term that describes the use of classification schemes, such as data algorithms, to sort or score workers in ways that worsen inequality or disadvantage along the lines of race, sex, or other protected characteristics. Addressing classification bias requires fundamentally rethinking antidiscrimination doctrine. When decision-making algorithms produce biased outcomes, they may seem to resemble familiar disparate impact cases; however, mechanical application of existing doctrine will fail to address the real sources of bias when discrimination is data-driven. A close reading of the statutory text suggests that Title VII directly prohibits classification bias. Framing the problem in terms of classification bias leads to some quite different conclusions about how to apply the antidiscrimination norm to algorithms, suggesting both the possibilities and limits of Title VII’s liability-focused model.
关键词：Employment Discrimination; Algorithms; Data Mining; Disparate Impact