Unveiling the Challenges of Artificial Intelligence Use in Auditing: A Holistic Multi-Criteria Decision-Making Approach

Abstract

This study adopts the Fermatean fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) method to systematically assess the challenges associated with the use of artificial intelligence (AI) in auditing. Data were collected from four domain experts who evaluated seven identified challenges, after which the proposed method was applied to determine their relative severity. The results reveal that transparency and explainability, robustness and reliability, and data privacy are the three most critical challenges related to the use of AI in auditing. The study makes a meaningful contribution to the decision sciences and management literature by providing practical insights for policymakers and professionals in the auditing domain. It concludes by outlining clear directions for future research.