The Divergence of Auditors’ Stated Risk Assessments and Planned Audit Responses to Clients’ Use of Artificial Intelligence

40 Pages Posted: 28 Nov 2023

See all articles by Nikki L. MacKenzie

Nikki L. MacKenzie

Georgia Institute of Technology - Scheller College of Business

Jennifer McCallen

University of Georgia - J.M. Tull School of Accounting

Jane M. Thayer

Georgia Institute of Technology

Date Written: October 31, 2023

Abstract

Artificial intelligence (AI) offers the potential for improvement in the reporting of complex estimates. However, both practitioners and researchers lack an understanding of how the use of AI by audit clients impacts auditor judgments when planning the audit of a complex estimate. In this study, we examine how the client’s valuation source (AI vs. human valuation expert) and the level of estimation accuracy in the prior period’s valuation (more vs. less) impacts auditors’ risk assessment judgments and subsequent planned audit response for the current year audit of a complex estimate. Our findings suggest that individual’s planned responses do not align with their stated risk assessments when AI is utilized to generate the estimate. Specifically, the level of estimation accuracy in the prior period drives auditors’ risk assessments for both human and AI-generated estimates. However, when AI-generated estimates are more (less) accurate in the prior period, auditors plan less (more) audit work compared to when estimates are generated by human experts, potentially leading to audit effectiveness or efficiency concerns. While auditors should be conducting risk-based audits, mediation results indicate auditors are susceptible to automation bias when clients use AI rather than a human expert in deriving the estimate.

Keywords: Complex estimates, artificial intelligence, risk assessment, audit planning, look-back analysis

Suggested Citation

MacKenzie, Nikki and McCallen, Jennifer and Thayer, Jane M., The Divergence of Auditors’ Stated Risk Assessments and Planned Audit Responses to Clients’ Use of Artificial Intelligence (October 31, 2023). Available at SSRN: https://ssrn.com/abstract=4619017 or http://dx.doi.org/10.2139/ssrn.4619017

Nikki MacKenzie (Contact Author)

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

Jennifer McCallen

University of Georgia - J.M. Tull School of Accounting ( email )

Athens, GA 30602
United States

Jane M. Thayer

Georgia Institute of Technology

Atlanta, GA 30332
United States

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