Jason R. Bent

Professor of Law

B.A., Grinnell College
J.D., University of Michigan

Phone: 727-562-7339
Email: [email protected]
Office: CR-210 (Gulfport)

Civil Procedure, Employment Discrimination Law, Employment Law, Remedies, Federal Courts, Worker Safety Law, and Policy Seminar


Jason R. Bent

Professor Bent focuses his work on employment law, employment discrimination law, and civil procedure. His scholarly interests include systemic theories of employment discrimination, federal workplace safety regulation, and the use of economic theory and statistical techniques in the development of legal doctrine. He co-authors a leading treatise on the use of statistics in employment discrimination cases, The Statistics of Discrimination: Using Statistical Evidence in Discrimination Cases. He is also a co-author of An Illustrated Guide to Civil Procedure. His most recent journal article focuses on efforts to counteract unintended bias when using algorithms in employment decisions and was selected for publication in the Georgetown Law Journal. His work has also been featured in the BYU Law Review, the Connecticut Law Review, the Ohio State Law Journal, the Buffalo Law Review, the Denver University Law Review, the Tennessee Law Review, and the Michigan Journal of Law Reform. Prior to joining the faculty at Stetson, Professor Bent was a Shughart Fellow and visiting assistant professor at the Penn State University Dickinson School of Law.

Featured Publications

Is Algorithmic Affirmative Action Legal?

This article evaluates the legality of the leading fairness techniques advanced in the machine learning literature, including group fairness, individual fairness, and counterfactual fairness. The article concludes that existing affirmative action doctrine under Title VII and existing constitutional equal protection jurisprudence leave sufficient room for some forms of algorithmic affirmative action.

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The Statistics of Discrimination: Using Statistical Evidence in Discrimination Cases

The Statistics of Discrimination: Using Statistical Evidence in Discrimination Cases provides the background in statistical analysis needed to apply such analysis to evolving areas of employment discrimination law. With co-author Ramona Paetzold.

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Additional publications are available on SSRN.

Professor Bent graduated magna cum laude from the University of Michigan Law School, where he served as a notes editor of the Michigan Law Review and was a member of the Order of the Coif. Professor Bent earned his bachelor's degree in economics, with honors, from Grinnell College. Following law school, Professor Bent served as a judicial clerk to Judge Cornelia Kennedy of the U.S. Court of Appeals for the 6th Circuit and Judge Joan B. Gottschall of the U.S. District Court for the Northern District of Illinois. Following his clerkships, Professor Bent practiced in the Labor and Employment and Appellate Practice Groups with Foley & Lardner LLP. He later became a principal and shareholder of Smith & Bent P.C., where he practiced employment law and environmental litigation. While in private practice, Professor Bent represented clients in International Chamber of Commerce arbitration proceedings, in systemic employment discrimination litigation involving the U.S. Equal Employment Opportunity Commission, and in a wide variety of state and federal litigation, private arbitration, and mediations. He also regularly assisted clients in interactions and negotiations with state human rights and environmental regulatory agencies.