Generative AI Training and Copyright Law
Not on the map. Why?
Abstract (arXiv)
Training generative AI models requires extensive amounts of data. A common practice is to collect such data through web scraping. Yet, much of what has been and is collected is copyright protected. Its use may be copyright infringement. In the USA, AI developers rely on "fair use" and in Europe, the prevailing view is that the exception for "Text and Data Mining" (TDM) applies. In a recent interdisciplinary tandem-study, we have argued in detail that this is actually not the case because generative AI training fundamentally differs from TDM. In this article, we share our main findings and the implications for both public and corporate research on generative models. We further discuss how the phenomenon of training data memorization leads to copyright issues independently from the "fair use" and TDM exceptions. Finally, we outline how the ISMIR could contribute to the ongoing discussion about fair practices with respect to generative AI that satisfy all stakeholders.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 44 references that are in the map. First posted on arXiv 21 Feb 2025.
Builds on
19 of 44 references.
References · 19
- 2022Quantifying Memorization Across Neural Language Modelscited by 763
- 2021Deduplicating Training Data Makes Language Models Bettercited by 714
- 2022High Fidelity Neural Audio Compressioncited by 687
- 2020Jukebox: A Generative Model for Musiccited by 542
- 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 460
- 2023MusicLM: Generating Music From Textcited by 448
- 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 434
- 2023High-Fidelity Audio Compression with Improved RVQGANcited by 431
- 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 424
- 2024Detecting, Explaining, and Mitigating Memorization in Diffusion Modelscited by 95
- 2024Long-form music generation with latent diffusioncited by 89
- 2024Towards Memorization-Free Diffusion Modelscited by 50
7 more
- 2024Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMscited by 50
- 2024A Geometric Framework for Understanding Memorization in Generative Modelscited by 40
- 2023From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusioncited by 22
- 2024AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollockscited by 13
- 2024Frame by Familiar Frame: Understanding Replication in Video Diffusion Modelscited by 11
- 2023Generation or Replication: Auscultating Audio Latent Diffusion Modelscited by 10
- 2024SymPAC: Scalable Symbolic Music Generation With Prompts And Constraintscited by 5
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2026Membership Inference Attack Against Music Diffusion Models via Generative Manifold Perturbationcited by 1
- 2025We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergycited by 0
- 2026Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidancecited by 0
- 2026Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance14 May 2026
- 2026Membership Inference Attack Against Music Diffusion Models via Generative Manifold Perturbation2 Feb 2026
- 2025We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergy10 Apr 2025