Tagging Lemons: The Strategic Use of AIGC Tags in Online Artwork Marketplaces

Abstract

Many platforms now host both user-generated content (UGC) and AI-generated content (AIGC), managing them through tagging mechanisms. However, little is known about how creators use these tags and what market dynamics their use may entail. This study examines the consequences of adherence to a voluntary AIGC tagging policy implemented in the online artwork marketplace. Using image-based detection and a staggered difference-in-differences design, we identify opportunistic artists who strategically omit tags on low-quality AIGC artworks to misrepresent them as human-generated. We find that such behavior helps consumers distinguish high-quality AIGC and artists, reducing sales of opportunistic artist artworks and thus mitigating adverse selection. We attribute this effect to consumers’ ability to detect speculative behavior. This explanation is corroborated by further computational image analysis. We also find that opportunistic behavior significantly lowers artwork quality, suggesting heightened moral hazard. These findings offer important theoretical and practical implications for platforms that manage AIGC.