题　目：Designing Detection Algorithms for AI-Generated Content: Consumer Inference, Creator Incentives, and Platform Strategy

嘉　宾：Jieteng Chen (陈杰腾), Ph.D. Candidate, 香港中文大学

主持人：左思 助理教授 上海交通大学安泰经济与管理学院

时　间：2025年10月8日（周三）9:00-10:30

地　点：上海交通大学 徐汇校区 安泰经济与管理学院A303

内容简介:

Generative AI has transformed content creation, enhancing efficiency and scalability across media platforms. However, it also introduces substantial risks, particularly the spread of misinformation that can undermine consumer trust and platform credibility. In response, platforms deploy detection algorithms to distinguish AI-generated from human-created content, but these systems face inherent trade-offs: aggressive detection lowers false negatives (failing to detect AI-generated content) but raises false positives (misclassifying human-created content), discouraging truthful creators. Conversely, conservative detection protects creators but weakens the informational value of labels, eroding consumer trust. We develop a model in which a platform sets the detection threshold, consumers form beliefs from content labels and decide whether to engage, and creators choose whether to adopt AI and how much effort to exert to create content. We find that as the detection threshold varies, the equilibrium structure undergoes a regime shift. At low thresholds, consumers trust human labels and partially engage with AI-labeled content, disciplining AI misuse and boosting engagement. But when the detection threshold becomes higher, this inference breaks down, AI adoption rises, and both trust and engagement collapse. Thus, the platform’s optimal detection strategy balances these risks, influencing content creation incentives, consumer beliefs, and overall welfare.

演讲人简介：

Jieteng Chen is a Ph.D. candidate in Marketing from the Chinese University of Hong Kong. He received his bachelor’s degree in Economics from Xiamen University in 2021. His research explores the economics of AI, digital platforms, and industrial organization, with a particular focus on algorithmic design by online platforms and the business implications of emerging technologies. His work has been published in Journal of Marketing Research, and he has an ongoing project at an advanced stage in Management Science. He also serves as an ad-hoc reviewer for Marketing Science. His research is supported by NSFC Research Grant for Ph.D. Students.

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