AI Autonomous Governance: Concept, Technical Foundation, and Conditions for Realization

Abstract

The cognitive and executive capabilities of artificial intelligence (AI) have matured considerably, yet “being able to execute” is not equivalent to “being able to govern.”On the basis of “artificial intelligence” and “AI autonomization,” this paper proposes the conceptual framework of “AI autonomous governance-enabling.” It argues that governance-enabling does not start from scratch; rather, it superimposes a “management” function on top of the knowledge foundation and the autonomized execution capability of mature AI, thereby forming a governance loop of “ex ante review—in-process monitoring and response—ex post retrospective and refinement.” The paper first argues that the essence of contemporary AI is a statistical inference engine built on the digitization of human knowledge; it constitutes the knowledge foundation for autonomous governance but does not in itself amount to governance capability. It then analyzes the shift in AI autonomization from reasoning to execution, clarifying the distinction between“autonomy in execution” and “governance autonomy.” It further elaborates that, as a leap from execution to management, autonomous governance-enabling is essentially autonomy in management rather than autonomy in execution. Finally, it points out that the legitimacy of autonomous governance-enabling is not automatically acquired; it must satisfy four conditions—transparency, auditability,intervenability, and traceability of responsibility—and be realized progressively through graduated authorization and human meta-governance. The paper concludes that the advancement of AI autonomous governance-enabling should uphold the distinction between “autonomy in execution” and “governance autonomy” and base governance-enabling on the coupling of technology,institutions, and norms. Its significance for AI research and practice lies in three points: it proposes a discriminating distinction between “autonomy in execution”and “governance autonomy,” delineating the governance boundaries for the design of autonomous systems; it characterizes the governance loop mechanism that can be mapped onto system modules; and it proposes a graduated authorization criterion based on “governance loop maturity,” providing operational guidance for the deployment and oversight of autonomous systems.