<![CDATA[The rapid integration of Artificial Intelligence (AI) into audit practice has revolutionized the professional process by improving accuracy and efficiency, especially in fraud detection, business continuity analysis, and risk assessment. However, there is no global consensus on how auditors assess and respond to AI outputs, leading to potential variability in audit quality. Auditors’ perceptions of the reliability and transparency of AI systems are key factors that shape their level of trust and maintain professional skepticism, which are significantly influenced by the cultural and regulatory contexts in different jurisdictions. This study aims to examine how auditors’ perceptions of the reliability and transparency of AI affect their level of reliance and skepticism in a cross-border audit context. Using a narrative literature review approach, this study explores scientific literature from leading databases such as Scopus, Web of Science, ScienceDirect, and Emerald Insight over the period 2013 to 2025. The data are analyzed thematically to identify key patterns and build a conceptual framework that integrates the Technology Acceptance Model, Trust in Automation Framework, and Audit Judgment and Decision-Making Framework. The results of the analysis show that positive perceptions of the reliability and transparency of AI significantly increase auditors’ propensity to rely on the technology. Factors such as explainable AI, user control, provider reputation, and organizational culture contribute to the formation of auditor trust. However, a high level of reliance without adequate professional skepticism can reduce audit quality. In addition, contextual factors such as differences in national culture, legal systems (rules-based versus principles-based), and regulatory frameworks also influence auditor responses to the use of AI. These findings emphasize the importance of balancing the use of AI with the application of professional skepticism to maintain audit integrity and quality. Practical implications include the need for clear regulatory guidelines, transparent AI system design, and comprehensive auditor training to optimize the use of technology without neglecting the principles of professionalism.]]>
