Agile Governance Practices in Artificial Intelligence:Categorizing Regulatory Approaches and Constructing a Policy Toolbox
The diverse and complex risks associated with artificial intelligence applications present regulatory challenges,emphasizing the need for agility in current AI governance.However,there is still a lack of systematic theoretical analysis and practical guidance on achieving agile regulation in terms of specific paths,mechanisms,and tools.Regulatory agencies,often constrained by bureaucratic approaches,face difficulties in responding to uncertainties.As a result,practical reforms often consider"classification"as a potential approach to adapt to complexity and enhance agility.Through a comparative analysis of AI development across diverse domains,along with a comprehensive review of international experiences,this paper establishes the dimensions for classifying AI governance risks and corresponding policy toolboxes.The study aims to provide regulatory practitioners a concise classification framework and a guide for combining policy tools.This enables regulatory practices to identify and categorize governance targets based on industry characteristics and locate suitable policy combinations within the corresponding toolbox.
NETL
NSTL
万方数据
维普
