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

Benjamin P. Commerford, Sean A. Dennis, Jennifer R. Joe, Jenny W. Ulla

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

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.

Original languageEnglish
Pages (from-to)171-201
Number of pages31
JournalJournal of Accounting Research
Volume60
Issue number1
DOIs
StatePublished - Mar 2022

Bibliographical note

Publisher Copyright:
© 2021 The Chookaszian Accounting Research Center at the University of Chicago Booth School of Business

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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