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.

中文翻译：

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人与机器：复杂的估计和审计员对人工智能的依赖

审计公司正在投资数十亿美元来开发人工智能 (AI) 系统，以帮助审计师执行具有挑战性的任务（例如，评估复杂的估计）。尽管公司认为人工智能会提高审计质量，但越来越多的研究文件表明，个人经常表现出“算法厌恶”——与人类建议相比，基于计算机的建议更倾向于贬低，尽管其他方面的建议是相同的。因此，我们进行了一项实验来检查算法厌恶如何体现在审计师的判断中。与理论一致，我们发现审计师从他们公司的人工智能系统（而不是人类专家）收到相互矛盾的证据，建议对管理层的复杂估计进行较小的调整，特别是当管理层使用相对客观的估计进行评估时（与实际情况相比）。主观）输入。我们的研究结果表明，审计师对算法厌恶的敏感性可能证明对专业和财务报表用户来说代价高昂。