Abstract: Artificial intelligence requires large,diverse,and high-quality data to train machine learning models,and collecting this real-world data can be very difficult and can threaten individual privacy,trigger bias or discrimination,and violate copyright.In practice,synthetic data,as an alternative solutionhas received widespread attention and is increasingly being used to train machine learning models.This paper explores the governance framework of synthetic data in AI training from the perspective of data jurisprudence,drawing on research from both data science and computer science.It first analyzes the logical premise of the importance of synthetic data in AI training from the normative level,i.e.,there is an obvious incompatibility between the protection of “small privacy” pursued by the personal information protection law and the demand for “big data” in AI training,which makes the deve-lopment of training data challenging,and the development of synthetic data for machine learning models challenging.The development of training data faces challenges,while existing legal and technological solutions suffer from ineffective governance.On this basis,the application scenarios and risk types of synthetic data in AI training are discussed.Finally,it is proposed to build a coherent legal governance framework for synthetic data in AI training from three aspects,guided by the “law 3.0 theory” and “data governance theory”:formulating rules for handling synthetic data,strengthening process governance of synthetic data,and developing assessment tools for synthetic data.

Key words: Artificial intelligence, Synthetic data, Law 3.0, Coherent governance, Data law