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Unjust enrichment as a remedy for AI's unauthorised use of protected data

Yangzi Li & Jyh-An Lee

Common Law World Review2026https://doi.org/10.1177/14737795251410287article
ABDC B
Weight
0.50

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 the 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 contend 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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https://doi.org/https://doi.org/10.1177/14737795251410287

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@article{yangzi2026,
  title        = {{Unjust enrichment as a remedy for AI's unauthorised use of protected data}},
  author       = {Yangzi Li & Jyh-An Lee},
  journal      = {Common Law World Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14737795251410287},
}

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Unjust enrichment as a remedy for AI's unauthorised use of protected data

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.