Balancing Deregulation and Risk: A Framework for AI Deployment in U.S. Municipal Governance

Abstract

Purpose: This study addresses the critical challenge of integrating artificial intelligence (AI) into U.S. municipal governance. It seeks to balance the innovation potential of AI, highlighted by federal initiatives like Executive Order 14179, against significant risks to data security, algorithmic fairness, and democratic accountability. Methods: The research analyzes and synthesizes a body of pertinent literature, policy documents, and existing governance frameworks. This methodological approach is designed to develop insights directly useful to municipal leaders. Findings: The article introduces the Municipal AI Governance Model (MAGM) as a unified framework for promoting innovation while minimizing risk. The model establishes a tiered governance structure as the basis for its recommendations, which are tailored to different AI deployments, regulatory environments, and stakeholder preferences. The findings suggest that successful AI deployment requires a comprehensive risk assessment framework, multi-stakeholder governance, and adaptive monitoring mechanisms. Conclusion: The MAGM provides a strategic implementation roadmap designed to build community trust and align with potential federal requirements. By focusing on improving governance efficiency without compromising public trust or accountability, this paper contributes to the expanding literature on algorithmic governance and offers practical insights for deploying AI in a changing policy context.