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

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 multidimensional 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