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Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence

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  • BENJAMIN P. COMMERFORD
  • SEAN A. DENNIS
  • JENNIFER R. JOE
  • JENNY W. ULLA

Abstract

Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion”—the tendency to discount computer‐based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm's AI system (instead of a human specialist) propose smaller adjustments to management's complex estimates, particularly when management develops their estimates using relatively objective (vs. subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.

Suggested Citation

  • Benjamin P. Commerford & Sean A. Dennis & Jennifer R. Joe & Jenny W. Ulla, 2022. "Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence," Journal of Accounting Research, Wiley Blackwell, vol. 60(1), pages 171-201, March.
    • Handle: RePEc:bla:joares:v:60:y:2022:i:1:p:171-201
    DOI: 10.1111/1475-679X.12407
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    References listed on IDEAS

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    4. Greiner, Ben & Grünwald, Philipp & Lindner, Thomas & Lintner, Georg & Wiernsperger, Martin, 2024. "Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study," Department for Strategy and Innovation Working Paper Series 01/2024, WU Vienna University of Economics and Business.
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