Author
Ms Wanjing Sun
Organisation/Institution
Tsinghua University School of Public Policy and Management
Country
CHINA
Panel
Intellectual Property Rights
Title
Generative AI Governance: Copyright Protection And Innovation Incentives – A Case Study of New York Times V. OpenAI
Abstract
Generative artificial intelligence (GenAI) has transformed the creation and dissemination of expressive works, offering unprecedented opportunities to democratize creativity and augment human expression. However, this technological revolution challenges the copyright framework and knowledge ecosystem of the creative industries. GenAI systems require training on massive high-quality datasets—often composed of copyrighted materials—raising complex legal questions regarding fair use, data mining legality, and the boundaries of fair use. Ongoing litigation worldwide underscores the difficulty of applying traditional copyright principles to machine learning models, particularly when existing doctrines were designed for human creators rather than algorithmic systems. This paper examines the legal landscape surrounding GenAI training through a case study of New York Times v. OpenAI, one of the most prominent copyright disputes in the AI era. The analysis develops a comparative stakeholder framework that illustrates fundamental differences between the traditional copyright paradigm and the emerging artificial general intelligence (AGI) paradigm, highlighting shifts in value creation, attribution, and economic incentives. Building on this framework, the paper proposes a litigation micro-mechanism designed for AI developers and content creators that aims to lower transaction costs, reduce litigation expenses, and facilitate more effective negotiations between parties. A comparative review of legal frameworks in the United States, China, and the European Union reveals divergent regulatory approaches to GenAI training, reflecting different approaches to innovation incentives and creator protections. The paper examines how international litigations, particularly New York Times v. OpenAI, may influence China's evolving AI governance framework. This cross-jurisdictional analysis demonstrates how precedents set in American courts could shape regulatory responses and industry practices in China's rapidly developing AI ecosystem, affecting both domestic innovation incentives and international technology competition. Finally, the paper advances a series of policy recommendations to reconcile creators' rights with global AI innovation. These include adopting structured licensing frameworks that provide clarity for AI developers, implementing extended collective licensing mechanisms to reduce transaction costs, and establishing constructive opt-out systems that respect creator autonomy while enabling technological progress. These proposals aim to foster a sustainable ecosystem in China that protects creative incentives while harnessing GenAI's transformative potential across diverse legal and cultural contexts.
Biography
Wanjing Sun is a post-doctoral researcher at Tsinghua University's School of Public Policy and Management and an assistant researcher at the Institute for Contemporary China Studies. She holds a J.D. from Northeastern University School of Law and an LL.M. from the University of Southern California. Her research focuses on AI governance, examining the legal and policy frameworks surrounding generative AI development, copyright protection in the age of machine learning, and the challenges of balancing innovation incentives with creator rights. She specializes in comparative analysis of AI regulation across different jurisdictions and explores mechanisms for effective global collaboration in technology governance.
