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Protecting AI-Generated Images as Works of Fine Art in China: Magnifying the Legacy of Art to Copyright

Abstract

Generative artificial intelligence (AI) challenges copyright by raising questions about whether AI-generated content (AIGC) qualifies as protectable works and who should own the associated rights. The Chinese judiciary addressed these issues in Li Yunkai v Liu Yuanchun (Li v Liu), a landmark case where the Beijing Internet Court ruled that an AI-generated image qualified as a work of fine art, with authorship vested in the user who entered the original prompts. This article evaluates the validity of the first holding within the judicial narrative about generative AIs. It argues that the Chinese judiciary maintains a policy to treat generative AIs as ordinary tools for creation. On this basis, while protecting AI-generated images as works of fine art aligns with China’s copyright system, the legal rationale must derive from artistic narratives of originality and aesthetics rather than the court’s reasoning in Li v Liu. Specifically, China should adopt a zero-originality threshold for works of fine art, recognising aesthetic effects as sufficient for protection, provided human intervention occurs through the refinement of AI outputs.

Published: 2025-04-29
Pages:107 to 126
Section: Symposium: Narratives, Frontier Technologies, and the Law (Part I)
How to Cite
Lin, Xi. 2025. “Protecting AI-Generated Images As Works of Fine Art in China: Magnifying the Legacy of Art to Copyright”. Law, Technology and Humans 7 (1):107-26. https://doi.org/10.5204/lthj.3825.

Author Biography

Maastricht University
Netherlands Netherlands

Xi Lin is a PhD candidate at the Institute for Globalisation and International Regulation at Maastricht University, the Netherlands. Her dissertation focuses on the interplay between eligibility criteria for copyright subject matter and the practical application of copyright exceptions and limitations. She has published work on the empirical observation of the EU’s harmonization of copyright exceptions and limitations.

Open Access Journal
ISSN 2652-4074