Article,
Big Data's Disparate Impact
SSRN eLibrary, (2014)
Abstract
This article addresses the potential for disparate impact in the data mining processes that are taking over modern-day business. Scholars and policymakers had, until recently, focused almost exclusively on data mining’s capacity to hide intentional discrimination, hoping to convince regulators to develop the tools to unmask such discrimination. Recently there has been a noted shift in the policy discussions, where some have begun to recognize that unintentional discrimination is a hidden danger that might be even more worrisome. So far, the recognition of the possibility of unintentional discrimination lacks technical and theoretical foundation, making policy recommendations difficult, where they are not simply misdirected. This article provides the necessary foundation about how data mining can give rise to discrimination and how data mining interacts with anti-discrimination law.<br><br>The article carefully steps through the technical process of data mining and points to different places within the process where a disproportionately adverse impact on protected classes may result from innocent choices on the part of the data miner. From there, the article analyzes these disproportionate impacts under Title VII. The Article concludes that Title VII is largely ill equipped to address the discrimination that results from data mining. Worse, due to problems in the internal logic of data mining as well as political and constitutional constraints, there appears to be no easy way to reform Title VII to fix these inadequacies. The article focuses on Title VII because it is the most well developed anti-discrimination doctrine, but the conclusions apply more broadly because they are based on the general approach to anti-discrimination within American law.
