Unjust Enrichment as a Remedy for AI's Unauthorised Use of Protected Data

Yangzi Li; Jyh-An Lee

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arXiv · 2605.23503

Unjust Enrichment as a Remedy for AI's Unauthorised Use of Protected Data

Yangzi Li·Jyh-An Lee

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Abstract

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.

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Yangzi Li, Jyh-An Lee. 2026-05-22. Unjust Enrichment as a Remedy for AI's Unauthorised Use of Protected Data. https://arxiv.org/abs/2605.23503

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