Research Student
Working toward a PhD in Laws, expected 2023
Email:
Ms. Yangzi LI is a PhD candidate at the Faculty of Law, The Chinese University of Hong Kong (CUHK LAW). She received her LLM degree from CUHK LAW and LLB degree from Guangdong University of Foreign Studies. Her research areas include intellectual property, law & technology, artificial intelligence policy, and Chinese law. Her articles have appeared in academic journals, such as International Journal of Law and Information Technology (Oxford University Press), Journal of Intellectual Property Law & Practice (Oxford University Press), GRUR International—Journal of European and International IP Law (Oxford University Press), European Intellectual Property Review, and in edited book published by Brill. She has been invited to present at the academic conferences such as Asian Legal History Conference co-organized by CUHK LAW and Hue University in Vietnam, Asian IP Works-In-Progress Conference co-organized by Yong Pung How School of Law, Singapore Management University and School of Law, City University of Hong Kong. She has been a member of the Creative Commons Hong Kong Chapter since October 2018. She was a member in the discussion leadership group of Copyright and Right to Research Seminar & Lecture Series, Program on Information Justice and Intellectual Property (PIJIP), American University Washington College of Law.
Creative Artificial Intelligence Matrix: Exploring the Regulation of AI’s Use of Copyrighted Works in the Creative Industries, co-supervised by Professors Jyh-An LEE and Noam NOKED.
Ms Yangzi LI’s research interests include intellectual property, law & technology, artificial intelligence policy and Chinese law. Yangzi’s doctoral dissertation explores the intersection between copyright limitations and exceptions system, which permit the unauthorized use of copyrighted works for value or for free, and AI’s use of copyrighted works in the creative industries. Building upon a doctrinal study on the interaction between insufficient copyright L&E system and Creative AI, and comparative approach on demonstrating which kinds of L&E fit Creative AI better. Overall, her doctoral research significantly contributes to the literature and policymaking by undoing the knots on the legitimacy of Creative AI’s use of copyrighted works, providing plausible suggestions to refine the traditional copyright L&E system to fit emerging AI technologies.
Journal Papers
Book Chapters