Reliance on Algorithmic Estimates: The Joint Influence of Algorithm Adaptability and Estimation Uncertainty

• 成果类型: Article; Early Access
• 署名作者: Ulla, Jenny W.; Commerford, Benjamin P.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Kentucky
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2024-0109
• 发表日期: 2025
• 关键词: fair value measurements; source credibility; decision-making; Auditors; advice; trust; task; quantification; Justification; expertise

摘要

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).