大模型个人信息训练语料的合法性困境与标准再造OA

Personal information constitutes a vital component of training corpora for large language models(LLMs),as it is crucial to the de-velopment of their reasoning capabilities.Existing personal information protection norms are based on a rights-oriented approach,which strug-gles to adapt to the technical characteristics of LLMs—such as algorithmic black boxes,data mining,and disclosure mechanisms—and thus suffers from limitations including unequal subject status,conflicts in rights values,and passive defense measures.To address these issues,it is imperative to shift to a risk-oriented approach.Guided by this approach,establishing standards in three key areas—the flexible adaptation of informed consent,the transfer of process control responsibilities,and the mitigation of complete deletion requirements—can overcome the lag of previous norms.This shift enables the rational allocation of risk liabilities,enhances governance efficiency,and balances diverse values,ulti-mately forming a full-lifecycle personal information protection framework for LLM training.