Consumers Object to Algorithms Making Morally Relevant Tradeoffs Because of Algorithms’ Consequentialist Decision Strategies

54 Pages Posted: 18 Feb 2021 Last revised: 1 Jun 2021

See all articles by Berkeley J. Dietvorst

Berkeley J. Dietvorst

The University of Chicago Booth School of Business

Daniel M. Bartels

University of Chicago - Booth School of Business

Date Written: December 22, 2020

Abstract

Why do consumers embrace some algorithms and find others objectionable? The moral relevance of the domain in which an algorithm operates plays a role. The authors find that consumers believe that algorithms are more likely to use maximization (i.e. attempting to maximize some measured outcome) as a decision-making strategy than human decision makers (Study 1). Consumers find this consequentialist decision strategy to be objectionable in morally relevant tradeoffs and disapprove of algorithms making morally relevant tradeoffs as a result (Studies 2, 3a, & 3b). Consumers also object to human employees making morally relevant tradeoffs when they are trained to make decisions by maximizing outcomes, consistent with the notion that their objections to algorithmic decision makers stem from concerns about maximization (Study 4). The results provide insight into why consumers object to some consumer relevant algorithms while adopting others.

Keywords: Morality, Judgment, Decision making, Values, Consequentialism

Suggested Citation

Dietvorst, Berkeley and Bartels, Daniel M., Consumers Object to Algorithms Making Morally Relevant Tradeoffs Because of Algorithms’ Consequentialist Decision Strategies (December 22, 2020). Available at SSRN: https://ssrn.com/abstract=3753670 or http://dx.doi.org/10.2139/ssrn.3753670

Berkeley Dietvorst (Contact Author)

The University of Chicago Booth School of Business ( email )

Chicago, IL 60637
United States

Daniel M. Bartels

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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