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When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content

Juan Wu, Zhe, Zhang, Amit Mehra

TL;DR

This paper develops a formal economic model to study platform governance of AI-generated content under two regimes: non-disclosure and mandatory self-disclosure with imperfect enforcement. It jointly models heterogeneous creators, viewer credibility discounts, trust penalties, and endogenous enforcement to reveal that disclosure is optimal only in an intermediate region where AI value and cost savings are neither too small nor overwhelming. The analysis shows that as AI capability evolves, the platform may shift from strict deterrence to partial screening and eventual deregulation, with disclosure enhancing transparency but often reducing aggregate creator surplus and potentially suppressing high-quality AI content. The findings highlight that disclosure acts as a strategic tool that reallocates AI usage across creators and content domains, balancing transparency against productive efficiency and trust frictions in AI-intensive content markets.

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 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.

When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content

TL;DR

This paper develops a formal economic model to study platform governance of AI-generated content under two regimes: non-disclosure and mandatory self-disclosure with imperfect enforcement. It jointly models heterogeneous creators, viewer credibility discounts, trust penalties, and endogenous enforcement to reveal that disclosure is optimal only in an intermediate region where AI value and cost savings are neither too small nor overwhelming. The analysis shows that as AI capability evolves, the platform may shift from strict deterrence to partial screening and eventual deregulation, with disclosure enhancing transparency but often reducing aggregate creator surplus and potentially suppressing high-quality AI content. The findings highlight that disclosure acts as a strategic tool that reallocates AI usage across creators and content domains, balancing transparency against productive efficiency and trust frictions in AI-intensive content markets.

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 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.
Paper Structure (27 sections, 7 theorems, 34 equations, 4 figures, 1 table)

This paper contains 27 sections, 7 theorems, 34 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature review
  3. Platform Responses to AI-Generated Content
  4. AI usage incentive
  5. User Behavior and Platform Dynamics
  6. Model Setup
  7. Platform, Creators, and Revenue Sharing
  8. Content Creation Technologies
  9. Platform Moderation and Disclosure Policies
  10. Viewer Perceptions and AI Aversion
  11. Trustworthiness and Disclosure Compliance
  12. Creator Payoffs
  13. Non-disclosure regime.
  14. Self-disclosure regime.
  15. Timeline
  16. ...and 12 more sections

Key Result

Proposition 1

Under the non-disclosure regime (i) platform profit decreases in $v$ for $\underline{v}_1<v<\min\{\tilde{v}_1,\hat{v}\}$ and increases in $v$ for $\min\{\tilde{v}_1,\hat{v}\}<v<\bar{v}$. (ii) platform profit increases in $\beta$ for $\underline{v}_1<v<\min\{\tilde{v}_2,\hat{v}\}$ and decreases in $\

Figures (4)

  • Figure 1: Creators' choice of using Gen-AI under non-disclosure policy
  • Figure 2: the creators' segmentation under disclosure case
  • Figure 3: Creators' equilibrium strategies under Mandatory Disclosure Regime when $k=0.8,\beta=0.6,r=0.3,c=0.5$
  • Figure 4: Platform's optimal disclosure decision when $k=0.8,\beta=0.6,r=0.3,c=0.5$

Theorems & Definitions (7)

  • Proposition 1
  • Proposition 2
  • Corollary 1
  • Proposition 3
  • Proposition 4: Platform's optimal disclosure strategy
  • Proposition 5
  • Proposition 6