生成式人工智能训练数据著作权合规困境与治理路径研究

Research on the Copyright Compliance Dilemma and Governance Path of Generative Artificial Intelligence Training Data

摘要: 生成式人工智能的快速发展依赖海量训练数据，但其著作权合规问题面临制度性困境。现行规定的合理使用情形难以涵盖商业化训练行为：主体限定为个人或科研人员，与AI企业不符；海量复制突破“少量使用”边界。法定许可制度适用范围封闭，无法适配AI训练场景，且授权成本高昂、作品贡献度量化困难。司法实践中，“接触 + 实质性相似”标准适用受阻，平台注意义务边界不清，权利人面临举证难、维权成本高等问题。广州互联网法院“奥特曼案”印证了上述困境：法院认定生成内容侵犯复制权与改编权，但未明确训练数据使用行为本身的法律定性，同时区分了服务提供者与模型开发者的责任边界。为破解合规困境，应构建“有限合理使用 + 场景化判断 + 配套补偿”的复合治理框架：增设AI数据训练专门合理使用条款，优化法定许可付费机制，明确平台事前审查、事中防控与事后处置义务，建立集体管理组织授权与数据整体补偿机制，强化算法披露与行业自律。唯有在技术创新与著作权保护之间实现动态平衡，方能促进生成式人工智能健康有序发展。

Abstract: The rapid development of generative artificial intelligence relies on massive training data, but its copyright compliance faces institutional dilemmas. The Law can hardly cover commercial training practices: the subject matter is limited to individuals or scientific researchers, which does not align with AI enterprises; massive copying exceeds the boundary of “limited use”. The statutory license system has a closed scope of application, cannot adapt to AI training scenarios, and suffers from high licensing costs and difficulties in quantifying the contribution of individual works. In judicial practice, the “access plus substantial similarity” standard encounters obstacles in application, the boundary of platform’s duty of care is unclear, and right holders face problems such as difficulty in evidence production and high enforcement costs. The “Ultraman case” in Guangzhou Internet Court confirms the above dilemmas: the court held that the generated content infringed the reproduction right and adaptation right, but did not clarify the legal characterization of the training data use itself, while distinguishing the boundary of liability between service providers and model developers. To resolve the compliance dilemmas, a composite governance framework of “limited fair use + context-specific judgment + supporting compensation” should be constructed: adding a specific fair use clause for AI data training, optimizing the payment mechanism of statutory license, clarifying platforms’ obligations of pre-review, in-process prevention and post-event handling, establishing collective management organization authorization and overall data compensation mechanisms, and strengthening algorithm disclosure and industry self-discipline. Only by achieving a dynamic balance between technological innovation and copyright protection can the healthy and orderly development of generative artificial intelligence be promoted.