Title: Generative AI and Copyright: A Dynamic Perspective
Speaker: Prof. S. Alex Yang, London Business School
Time: 14:30 pm, June 17, 2024
Venue: Room 105，Building No.12, Wushan Campus
Introduction to the speaker:
S. Alex Yang is a professor of Management Science and Operations at London Business School. He received his PhD and MBA from the University of Chicago Booth School of Business, Master degree from Northwestern University, and Bachelor Degree from Tsinghua University. As an award-winning teacher, Alex teaches a wide range of classes in MBA, EMBA and executive programs. His teaching portfolio encompasses subjects such as data science, digital technology, value chain management, business resilience, supply chain finance and FinTech. Alex is an expert in supply chain management and finance, FinTech, and the interaction of operations, finance and technology. His recent work focuses on innovations in digital platforms. His research has appeared in top management and finance academic journals, including Management Science, Manufacturing & Service Operations Management, and the Journal of Financial Economics. He also holds editorial positions at several prestigious international academic journals. Beyond academic research, Alex actively engages with the wider public through popular media. His insights have gained recognition in major news outlets, including Bloomberg, The Economist, Financial Times, Forbes, and The Washington Post. Alex has consulted for and collaborated with companies including United Airlines, Citadel Investment Group, Didi Chuxing, and Alibaba Group. He has also served as an international consultant at Asian Development Bank, specializing in trade finance and FinTech. He is currently working with companies in the areas of supply chain finance, FinTech, platform governance, and data science.
Abstract：
The rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard); and 2) the eligibility of AI-generated content for copyright protection (AI-copyrightability). While both issues have ignited heated debates among academics and practitioners, most analysis has focused on their challenges posed to existing copyright doctrines. In this paper, we aim to better understand the economic implications of these two regulatory issues and their interactions. By constructing a dynamic model with endogenous content creation and AI model development, we unravel the impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare, and how these impacts are influenced by various economic and operational factors. For example, while generous fair use (use data for AI training without compensating the creator) benefits all parties when abundant training data exists, it can hurt creators and consumers when such data is scarce. Similarly, stronger AI-copyrightability (AI content enjoys more copyright protection) could hinder AI development and reduce social welfare. Our analysis also highlights the complex interplay between these two copyright issues. For instance, when existing training data is scarce, generous fair use may be preferred only when AI-copyrightability is weak. Our findings underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions and provide insights for business leaders navigating the complexities of the global regulatory environment.
