Automation Bias and Professional Skepticism in AI-Assisted Auditing: A Systematic Literature Review - Annisa Rizkyta Yulie; Naufal Adiwidya Ramawi; Alya Zalfa Adelinia; Mukhtaruddin, Mukhtaruddin (CV. ITTC INDONESIA, 2026)
This study aims to analyze the relationship between automation bias and professional skepticism in the context of AI-assisted auditing using a Systematic Literature Review (SLR) approach. The rapid development of Artificial Intelligence (AI) has significantly transformed auditing practices by enhancing efficiency and data analysis capabilities. However, the adoption of AI also introduces risks, particularly the tendency of auditors to overly rely on system-generated outputs (automation bias), which may reduce their level of professional skepticism. This study applies the PRISMA-guided SLR method to review articles published between 2021 and 2026 from various scientific databases. The findings indicate that while AI improves audit quality and efficiency, it also affects auditors’ cognitive behavior, especially in decision-making processes. Automation bias arises due to high trust in AI systems, limited understanding of algorithms, and pressure for efficiency. In this context, professional skepticism plays a crucial role as a control mechanism to mitigate bias and maintain audit quality. This study concludes that a balance between technological utilization and the application of professional skepticism, along with stronger governance and technological literacy, is essential to ensure the optimal use of AI in auditing.