Reliance on Algorithmic Estimates: The Joint Influence of Algorithm Adaptability and Estimation Uncertainty
通过两个实验，研究了算法适应性（机器学习特征）与估计不确定性如何共同影响会计专业人士对算法建议的依赖，发现高不确定性下审计师更愿依赖学习算法而非静态算法。
ABSTRACT Companies, including public accounting firms, are integrating systems with advanced algorithms into decision-making processes to assist with developing and evaluating complex estimates. However, individuals may hesitate to rely on algorithmic output, particularly under conditions of uncertainty. We conduct two experiments examining whether and how a system’s ability to adapt—an emerging feature of machine learning—interacts with uncertainty to influence accounting professionals’ reliance on algorithmic advice. In Experiment 1, we find that auditors are more willing to rely on advice from learning algorithms than static algorithms when estimation uncertainty is relatively high. Experiment 2 replicates this result in a general accounting context where preparers develop their own estimates. Our findings demonstrate that accounting professionals’ reliance on algorithms is contextually dependent, and highlights algorithm adaptability as an important technological feature that can promote advice utilization, particularly when adaptability is likely important to the judgment context (e.g., when estimation uncertainty is high). JEL Classifications: M40; M41; M42; O30; O32; O33.