Exploring remuneration mechanisms for copyrighted works in generative AI systems

Abstract Generative artificial intelligence (AI) reconfigures the foundations of copyright by introducing multi‐actor creation and platform‐mediated dissemination. Within a dual‐dimensional framework combining the expressive function of originality and the disseminative function of platform power, four scenarios are developed to calibrate incentive structures and institutional efficiency. Comparative analysis of US, EU, and China's judicial practices reveals divergent yet complementary approaches: US courts emphasize market substitution and fair use's fourth factor; the EU advances restriction‐based remuneration and extended collective licensing; and Chinese courts adopt a human‐centered authorship model that recognizes verifiable expressive contribution. Law‐and‐economics reasoning supports replacing exclusivity with incentive‐compatible tools—combining remuneration rights, collective management, and transparency obligations. Authorship is substantiated through process‐evidence chains, while platforms assume proportional compliance duties aligned with their market power. By integrating comparative jurisprudence into this expressive–disseminative framework, copyright governance can evolve from static ownership allocation toward dynamic incentive regulation, achieving balanced innovation, equitable distribution, and long‐term cultural sustainability.