生成式人工智能内容创作著作权风险与认定路径

Copyright Risks and Identification Paths of Generative AI-Generated Content

摘要: 数字时代到来，生成式人工智能技术于图文创作、文学艺术、音视频生产以及代码编写等领域，都有着关键作用的发挥，传统人力导向的创作方式，也出现了巨大变革，可AIGC内容创作是基于大数据与算法，其创作过程存在复杂的著作权风险，给现行著作权体系带去新挑战，本文选取AIGC内容生成的技术逻辑当作切入点，系统梳理AIGC生成过程中著作权侵权风险的类型。同时结合司法案例，明确AIGC侵权主体资格的认定标准，从归责原则、赔偿机制以及合理适用机制三个维度，提出适合AIGC技术特性的侵权责任分配路径，为制度创新与著作权保护的平衡以及相关法律制度的完善，提供理论与实践参考。

Abstract: With the advent of the digital era, generative artificial intelligence (AIGC) technology has become pivotal in diverse fields including visual content creation, literary arts, audiovisual production, and coding. This transformation has fundamentally reshaped traditional human-driven creative approaches. However, AIGC-generated content, which relies on big data and algorithms, presents complex copyright risks during its creation process, posing new challenges to existing copyright frameworks. This paper examines the technical logic of AIGC content generation as a starting point to systematically categorize copyright infringement risks in the AIGC creation process. By analyzing judicial cases, it clarifies the criteria for identifying liable parties in AIGC infringement. Furthermore, the paper proposes a liability allocation framework tailored to AIGC’s technical characteristics, addressing three dimensions: attribution principles, compensation mechanisms, and reasonable application standards. These insights provide theoretical and practical references for balancing institutional innovation with copyright protection and refining relevant legal systems.