A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Real Judgments

48 Pages Posted: 2 Feb 2024

See all articles by Jennifer Logg

Jennifer Logg

Georgetown University - McDonough School of Business

Rachel Schlund

Cornell University - School of Industrial and Labor Relations

Date Written: January 8, 2024

Abstract

We propose a simple explanation to reconcile research documenting algorithm aversion with research documenting algorithm appreciation: elicitation methods. We compare self-reports and judgments. When making judgments, people consistently utilize algorithmic advice more than human advice. In contrast, hypotheticals produce unstable preferences; people sometimes report indifference and sometimes report preferring human judgment. Moreover, people fail to correctly anticipate behavior, utilizing algorithmic advice more than they anticipate. A slight change in the framing of a hypothetical task additionally moderates algorithm aversion. Stated preferences about algorithms are less stable than judgments, suggesting that algorithm aversion may be less stable than previous research leads us to believe.

Keywords: Algorithms, Big Data, Judgment and Decision Making, Future of Work, Psychology of Technology, Theory of Machine

Suggested Citation

Logg, Jennifer and Schlund, Rachel, A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Real Judgments (January 8, 2024). Available at SSRN: https://ssrn.com/abstract=4687557 or http://dx.doi.org/10.2139/ssrn.4687557

Jennifer Logg (Contact Author)

Georgetown University - McDonough School of Business ( email )

Washington, DC
United States

Rachel Schlund

Cornell University - School of Industrial and Labor Relations ( email )

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
110
Abstract Views
301
Rank
442,217
PlumX Metrics