What does this research mean for the field?

Generative AI development requires enhanced, multi-layered copyright safeguards and cross-sectoral collaboration to address significant intellectual property vulnerabilities across both its input and output stages. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This review aims to explore copyright-related challenges posed by Generative AI and potential mitigation strategies.

July 1, 2026PeerJ Computer ScienceOpen Access

Generative AI and intellectual property: a systematic literature review of possible issues and mitigations

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This review aims to explore copyright-related challenges posed by Generative AI and potential mitigation strategies.
Conducted a systematic literature review from 2019 to mid-2025.
Focused on compliance challenges with EU copyright legislation in Generative AI.
Analyzed mitigation strategies such as watermarking, TDM opt-out mechanisms, and provenance tracking.
Identified key issues like opacity in training data usage and misalignment of content licenses.
Highlighted various mitigation strategies to enhance copyright protection, including watermarking and synthetic datasets.
Underlined the need for collaborative efforts to enhance copyright safeguards in the face of rapid AI development.

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Implication

Systematic review examines copyright issues in generative AI, highlighting mitigation strategies for compliance.

Cite This Study

Arnaudo et al. (2026) studied this question.

synapsesocial.com/papers/6a44af635cd2549c8bc4464e https://doi.org/10.7717/peerj-cs.3928

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Generative AI and intellectual property: a systematic literature review of possible issues and mitigations

Key Points

Discussion

Implication

Cite This Study

Generative AI and intellectual property: a systematic literature review of possible issues and mitigations

Key Points

Discussion

Implication

Cite This Study

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