Auditing Artificial Intelligence in Public Management: A Framework for Accountability, Transparency, and Ethical Governance

Abstract

The integration of Artificial Intelligence (AI) in public management has transformed governmental operations, decision-making processes, and service delivery mechanisms. However, this technological adoption introduces unprecedented challenges in accountability, transparency, bias mitigation, and ethical governance. This article proposes a comprehensive framework for auditing AI systems in public sector contexts, addressing both technical and governance dimensions. Through systematic literature review and analysis of emerging regulatory frameworks, we identify critical audit domains including algorithmic transparency, data governance, decision explicability, bias detection, and compliance verification. The study examines case implementations in public procurement, social services, and administrative decision-making, drawing insights from international experiences and regulatory developments. Key findings reveal that effective AI auditing requires multi-dimensional approaches combining technical validation, ethical assessment, legal compliance verification, and stakeholder engagement. The proposed framework establishes methodological guidelines for internal auditors, external oversight bodies, and governance structures, emphasizing risk-based auditing strategies aligned with public sector accountability principles. This research contributes to the emerging discourse on AI governance in public administration by providing practical audit methodologies adaptable to diverse governmental contexts while maintaining democratic values and citizen protection standards.