When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content
Juan Wu, Zhe (James) Zhang, Amit Mehra

TL;DR
This paper models the strategic use of AI content disclosure policies on digital platforms, revealing that optimal transparency depends on AI technology level, content value, and enforcement effectiveness, with implications for platform governance.
Contribution
It introduces a formal economic model analyzing the strategic incentives for AI content disclosure under imperfect enforcement and technological evolution.
Findings
Disclosure is optimal at intermediate AI value and cost-saving benefits.
Stricter enforcement shifts from deterrence to partial screening as AI improves.
Disclosure can reduce creator surplus and suppress high-quality AI content.
Abstract
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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Privacy, Security, and Data Protection · Copyright and Intellectual Property
