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

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 specialist 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.