Adoption Readiness and Trustworthy AI Governance across Public Administration Agencies

Abstract

The integration of artificial intelligence into public administration promises unprecedented efficiency and enhanced service delivery, yet it introduces profound challenges regarding accountability, transparency, and public trust. This paper investigates the critical intersection between trustworthy artificial intelligence governance structures and the institutional readiness of public agencies to adopt these transformative technologies. By employing advanced graph analysis techniques on inter-agency communication and policy-sharing networks, this study maps the relational dynamics that facilitate or hinder technology adoption in the public sector. The analysis focuses on how central positioning within a governance knowledge network influences an agency's overall adoption readiness. Through a comprehensive empirical examination of public administration agencies, the findings reveal that institutional proximity to central governance nodes significantly accelerates adoption readiness, mitigating perceived risks associated with algorithmic decision-making. Furthermore, the presence of dense, cohesive clusters within the network correlates with higher standardized implementations of ethical guidelines. This research contributes to the literature by demonstrating that adoption readiness is not merely an internal organizational attribute but a fundamentally networked phenomenon driven by structural ties in governance frameworks. The insights provided offer crucial guidance for policymakers seeking to foster responsible and efficient artificial intelligence deployment across diverse governmental bodies.