讲座主题：Labelling AI-Generated Content on Platforms: Misinformation, Consumer Inference, and Creator Incentives

主讲嘉宾：柯特 香港中文大学商学院

讲座时间：2026年9月22日周二14:30-17:00

讲座地点：腾讯会议542 751 183

嘉宾简介：柯特是香港中文大学商学院市场学教授兼系主任、运营决策与科技系教授（礼任）、经济系教授（礼任）。他在加州大学伯克利分校取得运筹学博士、统计学硕士、和经济学硕士学位，以及在北京大学取得物理学学士、和统计学学士学位。在2020年加入香港中文大学之前，他曾在麻省理工学院斯隆管理学院和运筹研究中心担任助理教授五年。他曾荣获香港中文大学青年研究员奖、杰出研究员奖、和杰出学者奖。柯特教授关于数字经济的研究于2024年获得国家自然科学基金优秀青年科学基金项目资助，是研讨“国家自然科学基金工商管理学科发展战略及‘十五五’发展规划”的受邀专家。他的研究领域涵盖量化营销模型、微观经济理论和产业组织。他目前担任《Management Science》《Marketing Science》《Journal of Marketing Research》和《Quantitative Marketing and Economics》的副主编。

内容摘要：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 identify and label AI-generated content, distinguishing it from human creations. These systems face inherent trade-offs: aggressive labeling lowers false negatives (failing to detect AI-generated content) but raises false positives (misclassifying human-created content), discouraging truthful creators; conversely, conservative labeling protects creators but weakens the informational value of labels, eroding consumer trust. We develop a model in which a platform sets the labeling threshold, consumers infer credibility from labels when deciding whether to engage, and both truthful and deceptive creators choosewhether to adopt AI and how much effort to exert to create content. Our model generates an endogenous association between AI labels and misinformation, through the mechanism that deceptive creators gain more from adopting AI, which is modeled as a generic productivity-enhancing technology. Equilibrium structure shifts across regimes as the platform's labeling threshold changes. At low thresholds, consumers trust human labels and partially engage with AI-labeled content, disciplining AI misuse and boosting engagement. At high thresholds, this inference breaks down, AI adoption rises, and both trust and engagement collapse. The platform’s optimal labeling threshold balances these forces by preserving label credibility while aligning creator incentives with consumer trust. Our analysis shows how labeling policy shapes content creation, consumer inference, and overall welfare in two-sided content markets.