Generative artificial intelligence (Gen-AI) is reshaping content creation on digital platforms by reducing production costs and enabling scalable output of varying quality. In response, platforms have begun adopting disclosure policies that require creators to label AI-generated content, often supported by imperfect detection and penalties for non-compliance. This paper develops a formal model to study the economic implications of such disclosure regimes. We compare a non-disclosure benchmark, in which the platform alone detects AI usage, with a mandatory self-disclosure regime in which creators strategically choose whether to disclose or conceal AI use under imperfect enforcement. The model incorporates heterogeneous creators, viewer discounting of AI-labeled content, trust penalties following detected non-disclosure, and endogenous enforcement. The analysis shows that disclosure is optimal only when both the value of AI-generated content and its cost-saving advantage are intermediate. As AI capability improves, the platform's optimal enforcement strategy evolves from strict deterrence to partial screening and eventual deregulation. While disclosure reliably increases transparency, it reduces aggregate creator surplus and can suppress high-quality AI content when AI is technologically advanced. Overall, the results characterize disclosure as a strategic governance instrument whose effectiveness depends on technological maturity and trust frictions.

翻译：生成式人工智能（Gen-AI）通过降低生产成本并实现不同质量内容的规模化输出，正在重塑数字平台的内容创作生态。作为应对，平台开始推行披露政策，要求创作者对AI生成内容进行标注，这些政策通常依赖于不完善的检测技术以及对违规行为的处罚。本文构建了一个形式化模型，以研究此类披露制度的经济影响。我们比较了两种情形：一种是无披露基准情形，仅由平台检测AI使用；另一种是强制自我披露制度，创作者在不完善的监管下策略性地选择披露或隐瞒AI使用。模型纳入了异质性创作者、观众对AI标注内容的折扣评价、未披露行为被检测后的信任惩罚以及内生的监管力度。分析表明，只有当AI生成内容的价值及其成本节约优势均处于中等水平时，披露才是最优选择。随着AI能力提升，平台的最优监管策略将从严格威慑演变为部分筛选，最终走向放松监管。虽然披露制度可靠地提高了透明度，但它会降低创作者总剩余，并在AI技术高度成熟时可能抑制高质量AI内容的产出。总体而言，研究结果表明披露是一种策略性治理工具，其有效性取决于技术成熟度与信任摩擦水平。