Trustworthy Artificial Intelligence dalam Internal Auditing: Systematic Literature Review dan Kerangka Pengembangan Masa Depan

This study aims to map the discussion of Trustworthy Artificial Intelligence (TAI) in internal auditing research. A qualitative approach using a Systematic Literature Review (SLR) guided by PRISMA 2020 was employed to examine articles indexed in Scopus within the 2016–2026 search window. A total of 44 articles met the inclusion criteria, all of which were published between 2020 and 2026. The data were analyzed descriptively and thematically, focusing on publication trends, AI technologies, audit domains, and TAI dimensions. The reviewed literature indicates that AI is increasingly discussed as a tool to support anomaly and fraud detection. Meanwhile, discussions of trustworthiness primarily focus on accountability and governance (65.9% of articles) and transparency and explainability (59.1%), with considerably less attention given to fairness and ethics (15.9%) and human oversight (9.1%). These findings suggest that the literature remains fragmented between technical performance and non-technical governance aspects. This study provides an integrated mapping of AI technologies, audit domains, and TAI dimensions, distinguishes between auditing with AI and auditing of AI, and proposes a research agenda toward continuous AI assurance. As a review of published studies, the findings reflect the current state of the literature rather.