The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems

31 Pages Posted: 17 Feb 2021 Last revised: 5 Mar 2021

See all articles by Kathleen Creel

Kathleen Creel

Stanford University

Deborah Hellman

University of Virginia School of Law

Date Written: February 15, 2021

Abstract

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what “arbitrariness” means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data, are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain why this systemic exclusion is of moral concern and to offer a solution to address it.

Suggested Citation

Creel, Kathleen and Hellman, Deborah, The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems (February 15, 2021). Virginia Public Law and Legal Theory Research Paper No. 2021-13, Available at SSRN: https://ssrn.com/abstract=3786377

Kathleen Creel

Stanford University ( email )

Stanford, CA 94305
United States

Deborah Hellman (Contact Author)

University of Virginia School of Law ( email )

580 Massie Road
Charlottesville, VA 22903
United States

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