Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence
实验发现，当审计师从公司AI系统（而非人类专家）收到矛盾证据时，他们对管理层复杂估计的调整幅度更小，尤其在管理层使用客观输入时，表明算法厌恶可能损害审计质量。
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.