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

• 成果类型: Article
• 署名作者: Commerford, Benjamin P.; Dennis, Sean A.; Joe, Jennifer R.; Ulla, Jenny W.
• 署名单位: University of Kentucky; State University System of Florida; University of Central Florida; University of Delaware; Nevada System of Higher Education (NSHE); University of Nevada Las Vegas
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456
• DOI: 10.1111/1475-679X.12407
• 发表日期: 2022
• 页码: 171-201
• 关键词: fair value; source credibility; quantification; Managers; JUDGMENT; work; task; vary

摘要

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.