Algorithm Credulity: Human and Algorithmic Advice in Prediction Experiments

Mathieu Chevrier, Brice Corgnet, Eric Guerci and Julie Rosaz
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Eric Guerci: Université Côte d'Azur, CNRS, GREDEG, France
Julie Rosaz: CEREN EA 7477, Burgundy School of Business, Université Bourgogne Franche-Comté, Dijon, France

No 2024-03, GREDEG Working Papers from Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France

Abstract: This study examines algorithm credulity by which people rely on faulty algorithmic advice without critical evaluation. Using a prediction task comparing human and algorithm advisors, we find that participants are more likely to follow the same deficient advice when issued by an algorithm than by a human. We show that algorithm credulity reduces expected earnings by 13%. To explain this finding, we posit that people are more likely to perceive as credible an unpredictable and deficient piece of advice when produced by an algorithm than by a human. Overall, our results imply that humans might be particularly susceptible to the influence of deficient algorithmic advice.

Keywords: Algorithm credulity; algorithmic advice; intelligibility; trust; laboratory experiments (search for similar items in EconPapers)
JEL-codes: C92 D91 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2024-02, Revised 2024-12
New Economics Papers: this item is included in nep-ain, nep-cmp and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:gre:wpaper:2024-03

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