Social Space Governance in the Artificial Intelligence Era

With the pervasive penetration of artificial intelligence (AI) technologies, traditional paradigms of social space governance are undergoing a fundamental shift—from "digital governance" to "intelligent governance." In the governance space dimension, AI innovations such as AI-generated content (AIGC) and spatial intelligence have endowed digital twin spaces with unprecedented capabilities, transforming them from static reflections of physical reality into dynamic systems capable of proactive inference, simulation, and real-time optimization. This transition extends governance functions beyond mere representation to include predictive intervention and anticipatory regulation. On the dimension of governance subjects, algorithms have qualitatively mutated—from passive instruments of execution into "artificial agents" or "auxiliary governance actors" possessing autonomous learning, environmental adaptation, and predictive decision-making capacities. This mutation fosters an emergent symbiotic "human-machine collaboration," challenging established power structures and reconfiguring accountability boundaries. In this study, building upon this analysis of the data-to-intelligence governance transition, we examine—through case studies including Shanghai's "Quantum City" and Hangzhou's "City Brain"—the expansion logic and practical manifestations of multidimensional social space governance in the AI era. First, the governance space has expanded from a tripartite "physical-social-data" framework to a quadrilateral "physical-social-data-algorithm" structure. Spatial intelligence technologies and "real-world model" paradigms have positioned algorithms as the core of digital twin systems. Empowered by spatial intelligence, digital twin environments achieve heightened precision and synchronicity, enabling real-time and efficient interactions with physical spaces while demonstrating enhanced generative capacity and operational autonomy. These developments constitute the multidimensional spatial arena of public governance. Second, the governance subject has evolved from a "government-market-society" triadic relationship to a "government-market-society-intelligence" quadrilateral synergy. As AI agents gain greater autonomy, their subjectivity becomes increasingly manifested, elevating AI from a mere instrument to a co-constitutive governance actor that must operate in parallel with traditional subjects. This transformation necessitates fundamental theoretical and practical interpretations of the relationships among all governance stakeholders. This multidimensional expansion has engendered a series of novel challenges for public governance practices. First, AI and digital twin technologies have accelerated the convergence of the physical, social, and digital domains, yet this nascent "hybrid space" has precipitated profound normative conflicts in governance practices. Second, as AI transitions from a tool to an intelligent agent, algorithmic bias becomes more acute, and an "accountability vacuum" risk emerges within human-machine collaborative frameworks. Finally, persistent digital divides are metamorphosing into a new configuration—the "intelligence divide"—exacerbating social stratification. To address these emergent challenges, social space governance in the intelligence era requires innovative pathways. First, cross-spatial coordinative governance mechanisms must be constructed to enable the synergistic integration of virtual and physical domains, shifting from normative fragmentation to spatial order reconstruction. Second, a human-machine coordinative governance framework should be built upon technical foundations of "trustworthy AI" and institutional safeguards ensuring "ultimate human control." Third, governance must uphold a people-centered value orientation, ensuring that the benefits of intelligent governance are equally distributed across all citizens.