大语言模型的"科林格里奇困境"及其因应OA

The rapid development of Large Language Models(LLMs)is increasingly entrapping laws in the Collingridge Dilemma.This spatiotemporal mismatch between the pace of technological iteration and the legislative cycle renders laws incapable of both identifying and regulating the attendant risks,such as those related to data compliance and algorithmic bias.In response,this paper proposes constructing a modular institutional toolbox to create a closed-loop governance system.This system would span the entire process of risk identification,behavioral guidance,evidentiary support,and market-based incentives.Centered on the core principles of graded supervision and mutual recognition of review,this institutional toolbox integrates four functional modules:Rules and Standards,Technological Empowerment,Capacity Building,and Data Incentives.By leveraging entitlement incentives and a presumption of defect,it facilitates data proprietization and a transformation in algorithmic liability.Furthermore,through the presumption of compliance and the integration of ethics into law,it transforms soft law guidelines into recognized benchmarks for determining evidence and obligations in judicial proceedings.It fosters a paradigm shift from static legislation to dynamic adaptive governance,thereby laying a normative groundwork for the future Artificial Intelligence Act.