An Anatomy of Algorithm Aversion

29 Pages Posted: 21 Jun 2024

See all articles by Cass R. Sunstein

Cass R. Sunstein

Harvard Law School; Harvard University - Harvard Kennedy School (HKS)

Jared Gaffe

Harvard University - Harvard Law School

Date Written: June 14, 2024

Abstract

People are said to show "algorithm aversion" when (1) they prefer human forecasters or decision-makers to algorithms even though (2) algorithms generally outperform people (in forecasting accuracy and/or optimal decision-making in furtherance of a specified goal). Algorithm aversion also has "softer" forms, as when people prefer human forecasters or decision-makers to algorithms in the abstract, without having clear evidence about comparative performance. Algorithm aversion is a product of diverse mechanisms, including (1) a desire for agency; (2) a negative moral or emotional reaction to judgment by algorithms; (3) a belief that certain human experts have unique knowledge, unlikely to be held or used by algorithms; (4) ignorance about why algorithms perform well; and (5) asymmetrical forgiveness, or a larger negative reaction to algorithmic error than to human error. An understanding of the various mechanisms provides some clues about how to overcome algorithm aversion, and also of its boundary conditions.

Suggested Citation

Sunstein, Cass R. and Gaffe, Jared, An Anatomy of Algorithm Aversion (June 14, 2024). Available at SSRN: https://ssrn.com/abstract=4865492 or http://dx.doi.org/10.2139/ssrn.4865492

Cass R. Sunstein (Contact Author)

Harvard Law School ( email )

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Harvard University - Harvard Kennedy School (HKS) ( email )

79 John F. Kennedy Street
Cambridge, MA 02138
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Jared Gaffe

Harvard University - Harvard Law School ( email )

1563 Massachusetts Avenue
Cambridge, MA 02138
United States

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