Author Information

Ding Xiaodong, Professor, Law School, Renmin University of China; Vice Director, Law and Technology Institute of Renmin University of China

Abstract

Data fusion and aggregation are not only the foundation of the development of artificial intelligence, but also of great significance in eliminating artificial intelligence biases and promoting social equity. However, legal institution such as personal information protection, copyright licenses, and the enclosure of enterprise data may hinder data aggregation. The root cause of such problems lies in market failure and traditional path dependence. The law should focus on transitioning from pre confirmation of data rights to post confirmation of data rights, constructing a mechanism for reasonable use of data, and reconstructing the public nature of data. Personal information and copyrighted works should be moderately relaxed in the data collection end. For enterprise data, data crawlers that comply with behavioral norms should be allowed to mitigate certain legal responsibilities and play a bridging role as data crawlers. On the other hand, the law should strengthen artificial intelligence’s post-collection responsibility of to protect personal information, and prevent generative artificial intelligence from plagiarizing database. Data aggregation is similar to the aggregation of water droplets in reservoirs. The law should explore a data system that is in line with large-scale micro equity integration.

Keywords: Artificial Intelligence; Personal Information; Copyright; Data Interconnection; Large Scale Micro Equity