Understanding algorithm aversion: When is advice from automation discounted?

Andrew Prahl and Lyn Van Swol

Journal of Forecasting, 2017, vol. 36, issue 6, 691-702

Abstract: Forecasting advice from human advisors is often utilized more than advice from automation. There is little understanding of why “algorithm aversion” occurs, or specific conditions that may exaggerate it. This paper first reviews literature from two fields—interpersonal advice and human–automation trust—that can inform our understanding of the underlying causes of the phenomenon. Then, an experiment is conducted to search for these underlying causes. We do not replicate the finding that human advice is generally utilized more than automated advice. However, after receiving bad advice, utilization of automated advice decreased significantly more than advice from humans. We also find that decision makers describe themselves as having much more in common with human than automated advisors despite there being no interpersonal relationship in our study. Results are discussed in relation to other findings from the forecasting and human–automation trust fields and provide a new perspective on what causes and exaggerates algorithm aversion.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:36:y:2017:i:6:p:691-702

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