Peter Salib

Fri, May 6, 2022 12:00 PM – 1:00 PM CDT

Registration

Peter Salib will be presenting his proposal for "big data affirmative action" to address the challenges of inequality.

Abstract

As a vast and ever-growing body of social-scientific research shows, discrimination remains pervasive in the United States. In education, work, consumer markets, healthcare, criminal justice, and more, Black people fare worse than whites, women worse than men, and so on. Moreover, the evidence now convincingly demonstrates that this inequality is caused by discrimination, not other factors. Yet solutions are scarce. The best empirical studies find that popular interventions—like diversity seminars and anti-bias trainings—have little or no effect. And more muscular solutions—like hiring quotas or school bussing—are now regularly struck down as illegal. Indeed, in the last 30 years, the Supreme Court has invalidated every such ambitious affirmative action plan that it has reviewed.

This Article proposes a novel solution: Big Data Affirmative Action. Like old-fashioned affirmative action, Big Data Affirmative Action would award benefits to individuals because of their membership in protected groups. Since Black defendants are discriminatorily incarcerated for longer than whites, Big Data Affirmative Action would intervene to reduce their sentences. Since women are paid less than men, it would step in to raise their salaries. But unlike old-fashioned affirmative action, Big Data Affirmative Action would be automated, algorithmic, and precise. Circa 2021, data scientists are already analyzing rich datasets to identify and quantify discriminatory harm. Armed with such quantitative measures, Big Data Affirmative Action algorithms would intervene to automatically adjust flawed human decisions—correcting discriminatory harm, but going no further.

Big Data Affirmative Action has two advantages over the alternatives. First, it would actually work. Unlike, say, anti-bias trainings, Big Data Affirmative Action would operate directly on unfair outcomes, immediately remedying discriminatory harm. Second, Big Data Affirmative Action would be legal, notwithstanding the Supreme Court’s recent case law. As argued here, the Court has not, in fact, recently turned against affirmative action. Rather, it has consistently demanded that affirmative action policies both stand on solid empirical ground and be well-tailored to remedying only particularized instances of actual discrimination. The policies that the Court has recently rejected failed to do either. Big Data Affirmative Action can easily do both.

Speaker Bio

Peter N. Salib's research focuses on problems at the intersection of public law, economics, and artificial intelligence. His scholarship has been published or is forthcoming in, among others, The University of Chicago Law Review, the Northwestern University Law Review, and the Texas Law Review. He has presented his work at, among others, the Harvard/Yale/Stanford Junior Faculty Forum and the Harvard Law and Economics Workshop.

Before joining the University of Houston Law Center, Peter was a Climenko Fellow and Lecturer on Law at Harvard Law School. After graduating from law school, Peter clerked for the Honorable Frank H. Easterbrook and practiced law at Sidley Austin, LLP, where he specialized in appellate litigation.