Discussion on Implementation Path of Algorithm Governance in Audit Work

Abstract

At present, the application of algorithms in the decision-making process of enterprises has gradually expanded and changed audit work. Rule-based and machine-learning models have been used to address financial risk scoring, compliance monitoring and fraud detection, and as a result, new governance problems for the audit profession have arisen, such as technical opacity, accountability gaps and changing regulatory requirements. This paper studies the implementation paths of algorithm governance in audit systems, derives theoretical support from research on algorithmic accountability, and examines top-level governance standards as practical references. The five main implementation directions of the analysis are: algorithmic inventory and risk classification; model transparency and explainability protocols; bias detection and fairness validation; continuous monitoring and trigger-based Audit coverage; and organizational capacity building. In short, the paper suggests that to achieve effective algorithm governance in an auditing environment, a hybrid architecture is required, integrating technical controls, institutional accountability mechanisms and competency-oriented human supervision; together, these should ensure that algorithmic decisions are auditable, contestable and consistent with the professional standards for independent assurance.