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

Ben Commerford's paper "Man versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence," was accepted for publication in the Journal of Accounting Research. The paper is co-authored with Gatton Ph.D. graduates: Jenny Ulla, (UNLV) and Sean Dennis (UCF) as well as Jennifer Joe from the University of Delaware. 

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 (versus subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.