| Summary: | The emergence of generative AI presents complex challenges to existing copyright regimes, particularly concerning the large-scale use of copyrighted materials in model training. Legal disputes across jurisdictions highlight the urgent need for a balanced, principle-based framework that protects the rights of creators while fostering innovation. In China, a regulatory approach of “moderate leniency” has emerged—emphasizing control over downstream AI-generated content (AIGC) while adopting a more permissive stance toward upstream training. This model upholds the idea–expression dichotomy, rejecting theories such as “retained expression” or “retained style”, which improperly equate ideas with expressions. A critical legal distinction lies between real-time training, which is ephemeral and economically insignificant, and non-real-time training, which involves data retention and should be assessed under fair use test. A fair use exception specific to AI training is both timely and justified, provided it ensures equitable sharing of technological benefits and addresses AIGC’s potential substitutive impact on original works. Furthermore, technical processes like format conversion and machine translation do not infringe derivative rights, as they lack human creativity and expressive content. Even when training involves broader use, legitimacy may be established through the principle of technical necessity within the reproduction right framework.
|
|---|
