A Qualitative Exploration of Auditor Trust in Artificial Intelligence Audit Systems - Ardhani, Lutfi; Alabdullah, Tariq Tawfeeq Yousif; Ahmed, Essia Ries (JABE (JOURNAL OF ACCOUNTING AND BUSINESS EDUCATION), 2026)
he rapid integration of Artificial Intelligence (AI) into auditing practices has transformed how auditors perform analytical and judgmental tasks, yet it has also raised fundamental questions regarding trust in AI-driven audit tools. This study explores how auditors develop, maintain, and negotiate trust when interacting with AI systems characterized by “black box” decision-making. Using a qualitative research design, semi-structured interviews were conducted with professional auditors from diverse practices who have direct experience with AI-assisted audit tools. Thematic analysis revealed three key dimensions of trust formation: perceived transparency of algorithmic processes, auditors’ professional judgment and accountability concerns, and organizational norms surrounding AI implementation. Findings suggest that trust is neither static nor solely technology-driven but emerges through continuous cognitive and social negotiation between human expertise and algorithmic outputs. This study contributes to the growing literature on AI adoption in auditing by providing a conceptual model of auditor trust formation and offering practical insights for audit firms and technology developers aiming to enhance the interpretability, accountability, and acceptance of AI-based audit systems.