Artificial Intelligence in Social Governance: Global Insights from Theoretical Frameworks to Practical Applications Using CiteSpace
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Tsinghua University
Abstract
This study presents a comprehensive examination of the evolving role of artificial intelligence (AI) in social governance through an integrated bibliometric, theoretical, and case-based methodology. Utilizing CiteSpace-based keyword co-occurrence and burst analyses of data from 2014 to 2024, it identifies ten thematic clusters that illustrate a dual developmental trajectory: a vertical deepening of governance theories and technological paradigms, and a horizontal expansion into domains such as smart cities, environmental governance, and algorithmic administration. A critical review of international theoretical frameworks—including the Technology Acceptance Model, Diffusion of Innovation Theory, Socio-Technical Systems Theory, Algorithmic Governance Theory, and Digital Governance Theory—reveals both their analytical value and limitations in the context of Chinese governance. Comparative case studies, encompassing domestic initiatives such as “City Brain,” smart communities, smart courts, and digital villages, alongside international examples from Singapore, Estonia, and the European Union, highlight China's rapid policy-driven advancements as well as enduring challenges in algorithmic transparency, cross-departmental data integration, and public participation. The study identifies four key governance challenges: data security and privacy, algorithmic ethics and transparency, the digital divide, and coordination inefficiencies. Looking forward, it outlines future trajectories shaped by multimodal AI, generative AI, and digital twin technologies. Policy recommendations call for standardized data governance protocols, robust AI ethics frameworks, targeted digital literacy programs, and enhanced multi-stakeholder collaboration. The findings advance a practice-oriented, interdisciplinary research agenda for intelligent social governance and provide actionable insights for aligning technological innovation with institutional transformation in the AI era.
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