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

2026-05-22

AI Summary

This study addresses the legal challenges posed by generative AI models’ unauthorized use of protected data, which creates tensions under intellectual property and privacy laws. It systematically argues for the first time that the doctrine of unjust enrichment offers a viable remedial framework in such contexts, shifting focus away from traditional fault- or harm-based tort paradigms toward the gains developers obtain through illicit data utilization. Drawing on doctrinal analysis, comparative legal insights, and case-based reasoning, the paper integrates principles from intellectual property law, privacy law, and equity to formulate an actionable, restitution-centered remedy. This proposed framework demonstrates theoretical superiority over existing statutory and equitable remedies and offers a novel pathway to balance the rights of data holders with the imperatives of AI innovation.

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.

Problem

• unjust enrichment
• unauthorized data use
• generative AI
• intellectual property
• privacy laws

Innovation

• unjust enrichment
• generative AI
• data rights
• restitution
• legal remedy