<![CDATA[This research investigates artificial intelligence regulation in public administration through systematic qualitative analysis of literature examining governance frameworks, implementation experiences, and theoretical foundations relevant to government management of algorithmic systems. The study identifies significant gaps between regulatory aspirations and implementation realities, revealing that fewer than forty percent of AI governance mandates are verifiably implemented across federal agencies. Key findings reveal three interrelated governance challenges: transparency and accountability deficits preventing meaningful citizen oversight, organizational capacity barriers constraining implementation across government organizations, and tensions between innovation encouragement and protection of ethical principles. The research applies principal-agent theory, organizational learning frameworks, and public value theory to understand these governance challenges, concluding that effective AI regulation requires integrated approaches combining technical solutions with stakeholder engagement, organizational capacity investment, and democratic deliberation about public values. The study emphasizes that sustainable AI governance depends upon treating public value creation as the ultimate evaluation criterion and establishing governance mechanisms balancing innovation with meaningful constraint]]>
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