Audit and Artificial Intelligence: Audit data analytics and auditing AI

This PhD dissertation explores the intersection of Artificial Intelligence (AI) and auditing, focusing on both the adoption of AI technologies by audit firms and the auditing of AI-driven systems by external auditors, IT auditors, and public sector auditors. The dissertation consists of a brief synopsis and three articles designed to stand alone and can be read independently. The first article examines how audit firms integrate AI into financial statement audits, including fraud and misstatement identification, with particular emphasis on the barriers preventing auditors from adopting Advanced Audit Data Analytics. The second article investigates the role of public sector auditors in assessing ministries that have implemented AI in their operations, evaluating potential audit risks, and analyzing the development of audit criteria for AI systems. The third article investigates how external auditors flag critical audit matters related to AI technologies in the auditors reports for public financial statements in the United States. The dissertation is grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and the A Statement of Basic Auditing Concepts (ASOBAC) conceptual framework. A mixed-methods approach is employed, incorporating literature and document analysis (audit frameworks and financial statements), text mining, and interviews to provide comprehensive insights into this evolving field.