Data-Driven Discrimination at Work
William & Mary Law Review, Vol. 48, pp. 857-936 (2017)
Washington University in St. Louis Legal Studies Research Paper No. 16-12-01
80 Pages Posted: 28 Jun 2016 Last revised: 10 May 2017
Date Written: April 19, 2017
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, like data algorithms, to sort or score workers in ways that worsen inequality or disadvantage along the lines or 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.
Keywords: employment discrimination, algorithms, data mining, data analytics, workforce analytics, disparate impact, classification bias
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