The unauthorised use of data in the training of generative AI models presents significant legal challenges, particularly under intellectual property (IP) and privacy laws. These frameworks frequently grapple with the intricate relationship between data ownership and AI innovation, resulting in ongoing debates regarding optimal protection and enforceability. This article delves into considerable potential of unjust enrichment as an alternative legal doctrine for resolving disputes arising from such unauthorised data use. We explore how the concept of unjust enrichment captures the wrongfulness of unauthorised data use in a manner distinct from IP infringement and privacy violations. Furthermore, we analyse the extent to which gain-based restitution for unjust enrichment may prove more advantageous than existing remedies, including legal, equitable, and statutory options. We content that by shifting the emphasis from establishing wrongful conduct to recovering benefits obtained unjustly, unjust enrichment offers a pragmatic and equitable framework that reconciles the rights of data owners with the interests of AI developers.

翻译：生成式人工智能模型训练中未经授权使用数据的行为引发了重大法律挑战，尤其在知识产权与隐私法领域。这些法律框架常常难以平衡数据所有权与人工智能创新之间的复杂关系，导致关于最优保护路径及可执行性的持续争议。本文深入探讨了不当得利作为替代性法律原则在解决此类未经授权数据使用纠纷中的巨大潜力。我们解析了不当得利概念如何以区别于知识产权侵权与隐私侵犯的独特方式，捕捉未经授权数据使用的不当性。进而分析了基于获益的不当得利返还相较于现有法律救济（包括普通法、衡平法及法定救济）的潜在优势。我们认为，通过将重点从证明不当行为转向追回不当获取的利益，不当得利提供了兼顾数据所有者权利与人工智能开发者利益的务实且公平的框架。