A Regulatory Placebo? The Systemic Failure of Mandatory GenAI Labeling
2026-08-17 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors look at how many places are now requiring labels on AI-generated content because of fear and pressure from institutions. They show that these labeling rules are hard to apply and might slow down AI progress. By studying three main ideas behind these rules, they find that regulators don't fully understand how AI technology works. The authors argue these labels don't really solve problems and suggest we should focus more on what the AI produces, not just labeling its source.
Generative Artificial IntelligenceMandatory LabelingTechnology RegulationValue Dilution TheoryInformation Authenticity TheoryProactive Regulation TheoryRegulatory ComplianceAI GovernanceContent Governance
Authors
Jingyi Chen, Chaofan Bu, Shibo Yan, Xuesong Li
Abstract
We examine the worldwide trend of mandatory labeling of generative artificial intelligence(GenAI) as a reactive, symbolic form of legislation triggered by technological panic and institutional responses. From a technical perspective, this study demonstrates that current mandatory labeling not only creates implementation dilemmas but also risks hindering the evolutionary trajectory of AI technology. We then systematically analyze the three dominant theoretical strands of this regime, the value dilution theory, the information authenticity theory, and the proactive regulation theory, and find that they are products of regulators' cognitive limitations in understanding the logic of modern technology. Not only do such formalistic compliance requirements become a regulatory placebo, but they also obscure the genuine legal demands of the technological era. This challenges the current governance paradigm and suggests a shift from identity-label governance to content governance, with an urgent need to address the complex problems associated with GenAI.