报告题目：Managing Online Content Platforms with AIGC

报 告 人： 邓淇元

报告时间：2026年11月12日（星期四）10:00-11:30

报告地点：明哲楼517

主办单位：东北财经大学现代供应链管理研究院

【报告人简介】

Qiyuan Deng is an Assistant Professor of Information Systems and Operations Management at the School of Management and Economics, The Chinese University of Hong Kong, Shenzhen. She received her Ph.D. in Operations Management from Singapore Management University. Her research focuses on digital platforms, e-commerce, online retailing, and generative AI. She uses game theory, optimization, and econometrics to examine strategic and operational decisions involving firms, platforms, and consumers. Her work has appeared in Production and Operations Management and Manufacturing & Service Operations Management.

【摘要】

Generative AI can lower the cost of producing online content while creating new challenges for transparency and platform governance. When audiences respond less favorably to AI involvement, creators may conceal their use of these tools. Platforms must then balance the commercial value of content against the cost of detecting undisclosed AI use and the risk of regulatory sanctions. This study examines how external enforcement shapes platforms’ investment in AI detection and their choice of content governance policies. We develop a game-theoretic model linking detection investment, creators’ production and disclosure decisions, and the platform’s subsequent content promotion. The analysis compares two responses to detected undisclosed AI-generated content. Under relabeling, the content can circulate with a corrected AI label. Under removal, it is excluded from circulation. These policies differ in their effects on the value of retained content and creators’ incentives to disclose. We examine whether stronger enforcement necessarily induces greater detection investment, when removal is preferable to relabeling, and how promotion affects creators’ responses. By connecting external enforcement with platforms’ governance and promotion decisions, the study clarifies the economic trade-offs involved in governing AI-generated content and offers implications for platform policy and the design of disclosure enforcement.