Towards AI-Augmented Public Audit Systems: A Policy and Implementation Roadmap for Us Government Agencies and Multilateral Organizations

Vollständiger Abstract

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The integration of artificial intelligence into public sector audit and accountability functions represents one of the most consequential governance transformations of the early twenty-first century. Government agencies charged with fiscal oversight, program integrity monitoring, and financial accountability now confront a landscape in which the volume, velocity, and complexity of financial transaction data have fundamentally outpaced the capacity of traditional audit methodologies to deliver timely and comprehensive coverage. Artificial intelligence technologies, including machine learning, natural language processing, network analytics, and intelligent process automation, offer substantial potential to augment the analytical capacity of public audit institutions, extend audit coverage to previously inaccessible transaction populations, and accelerate detection timelines from years to days or hours. However, the translation of this potential into operational capability within government audit contexts requires navigating complex technical, institutional, legal, and ethical challenges that differ substantially from the private sector environments in which many AI audit tools were originally developed. This paper develops a comprehensive policy and implementation roadmap for the deployment of AI augmented audit capabilities within United States government agencies and multilateral organizations. The paper synthesizes evidence from existing AI audit implementations across federal, state, and international audit contexts; analyzes the alignment of AI augmentation strategies with established frameworks from the Government Accountability Office, the Office of Management and Budget, and the International Organization of Supreme Audit Institutions; and develops an original conceptual framework designated the AI-Augmented Audit Continuum (AIAC) to guide progressive capability development from foundational analytics to autonomous audit functions. The roadmap addresses four core implementation domains: technical infrastructure and data architecture requirements for AI-enabled audit; human capital and organizational change management for audit workforce transformation; governance, ethics, and risk management frameworks for accountable AI deployment; and policy and standards development to create enabling environments for AI-augmented oversight. The paper draws on recent advances in intelligent fraud monitoring, machine identity governance, adaptive risk scoring, and digital forensics analytics to ground its recommendations in the most current available evidence on AI audit capability development. The proposed framework argues that a structured three-phase implementation approach spanning 24 to 48 months enables federal audit agencies to achieve meaningful AI augmentation of core audit functions while managing implementation risk within acceptable bounds. Critical success factors include executive sponsorship at the agency leadership level, dedicated cross-functional implementation teams with embedded data science competencies, iterative pilot deployment strategies that generate performance evidence prior to enterprise-wide rollout, and robust governance structures that maintain human judgment at decision points with consequential implications for program beneficiaries and regulated entities. The paper concludes with specific policy recommendations addressing procurement, workforce development, standards alignment, and interagency coordination to accelerate responsible AI adoption across the federal audit ecosystem.