Theoretical framework and realistic path of AI-driven audit reform
The technical and economic characteristics of artificial intelligence(AI)determine that its application in the audit field will inevitably drive the update of audit concept,the embedding of audit data,the enhancement of audit technology and the innovation of audit enableability.Having gone through the stages of initial AI auditing,weak AI auditing,and strong AI auditing,the concept of AI auditing has gradually deepened,evolving from focusing on causality and correlation to now focusing on causality+correlation.Audit data has also undergone a leapfrog evolution from small data and big data to multimodal data.Audit technology has achieved a leapfrog development from expert models,decision models to large-scale models,and the series of changes have ultimately driven the comprehensive upgrade and all-round transformation of audit enablement from automation,digitization to intelligence.At present,AI auditing still faces mnay challenges in terms of conceptual transformation,data governance,algorithm trustworthiness,and efficiency utilization.It is necessary to objectively understand the impact of AI on audit,strengthen data governance,establish a large language model of audit knowledge,improve supporting institutions and mechanisms,and promote audit technology innovation and audit efficiency improvement on the whole.
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