When a Small Change Makes a Big Difference: Algorithmic Fairness Among Similar Individuals

83 Pages Posted: 12 Oct 2021 Last revised: 28 Apr 2022

See all articles by Jane R. Bambauer

Jane R. Bambauer

University of Florida Levin College of Law; University of Florida - College of Journalism & Communication; University of Arizona - James E. Rogers College of Law

Tal Zarsky

University of Haifa - Faculty of Law

Jonathan Mayer

Princeton University, School of Engineering and Applied Science, Department of Computer Science; Princeton University, Woodrow Wilson School of Public and International Affairs

Date Written: April 28, 2022

Abstract

If a machine learning algorithm treats two people very differently because of a slight difference in their attributes, the result intuitively seems unfair. Indeed, an aversion to this sort of treatment has already begun to affect regulatory practices in employment and lending. But an explanation, or even a definition, of the problem has not yet emerged. This Article explores how these situations—when a Small Change Makes a Big Difference (SCMBDs)—interact with various theories of algorithmic fairness related to accuracy, bias, strategic behavior, proportionality, and explainability. When SCMBDs are associated with an algorithm’s inaccuracy, such as overfitted models, they should be removed (and routinely are.) But outside those easy cases, when SCMBDs have, or seem to have, predictive validity, the ethics are more ambiguous. Various strands of fairness (like accuracy, equity, and proportionality) will pull in different directions. Thus, while SCMBDs should be detected and probed, what to do about them will require humans to make difficult choices between social goals.

Keywords: AI, artificial intelligence, algorithmic fairness, algorithmic bias, privacy, rules v standards

Suggested Citation

Yakowitz Bambauer, Jane R. and Zarsky, Tal and Mayer, Jonathan, When a Small Change Makes a Big Difference: Algorithmic Fairness Among Similar Individuals (April 28, 2022). 55 UC Davis Law Review 2337 (2022), Arizona Legal Studies Discussion Paper No. 21-23, Available at SSRN: https://ssrn.com/abstract=3940705

Jane R. Yakowitz Bambauer (Contact Author)

University of Florida Levin College of Law ( email )

P.O. Box 117625
Gainesville, FL 32611-7625
United States

University of Florida - College of Journalism & Communication ( email )

United States

University of Arizona - James E. Rogers College of Law ( email )

P.O. Box 210176
Tucson, AZ 85721-0176
United States

Tal Zarsky

University of Haifa - Faculty of Law ( email )

Mount Carmel
Haifa, 31905
Israel

Jonathan Mayer

Princeton University, School of Engineering and Applied Science, Department of Computer Science ( email )

35 Olden Street
Princeton, NJ 08540
United States

Princeton University, Woodrow Wilson School of Public and International Affairs ( email )

Princeton University
Princeton, NJ 08544-1021
United States

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