Risk aversion (psychology)

Quality (philosophy)

Philosophy

Quality audit

Actuarial science

Expected utility hypothesis

Accounting

Economics

Computer science

Business

Audit

Epistemology

Econometrics

Artificial intelligence

Financial economics

Programming language

Advice (programming)

Journal of Accounting Research

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.

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