ChatGPT: A Case Study on Copyright Challenges for Generative Artificial Intelligence Systems
Open accessAbstract
This article focuses on copyright issues pertaining to generative artificial intelligence (AI) systems, with particular emphasis on the ChatGPT case study as a primary exemplar. In order to generate high-quality outcomes, generative AI systems require substantial quantities of training data, which may frequently comprise copyright-protected information. This prompts inquiries into the legal principles of fair use, the creation of derivative works and the lawfulness of data gathering and utilisation. The utilisation of input data for the purpose of training and enhancing AI models presents significant concerns regarding potential violations of copyright. This paper offers suggestions for safeguarding the interests of copyright holders and competitors, while simultaneously addressing legal challenges and expediting the advancement of AI technologies. This study analyses the ChatGPT platform as a case example to explore the necessary modifications that copyright regulations must undergo to adequately tackle the intricacies of authorship and ownership in the realm of AI-generated creative content.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
This hand-waving appears mostly a thinly disguised attempt to obscure the clear and present harms such models already pose while minimising the considerable legal and regulatory exposure that would come with disclosing details about training data.
“but this appears mostly a thinly disguised attempt to obscure the clear and present harms such models already pose [17] while minimising the considerable legal and regulatory exposure that would come with disclosing details about training data [35, 49].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [35]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3389497 · Cited by 759 · see citing works
Generative Adversarial Networks
Salvaris, M., Dean, D., & Tok, W. H. (2018) · Apress eBooks · doi:10.1007/978-1-4842-3679-6_8 · Cited by 411 · see citing works
Why open-source generative AI models are an ethical way forward for science
Spirling, A. (2023) · Nature · doi:10.1038/d41586-023-01295-4 · Cited by 104 · see citing works
Schetinger, V., Bartolomeo, S. D., El‐Assady, M., McNutt, A. M., Miller, M., Passos, J. P. A., & Adams, J. L. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14841 · Cited by 54 · see citing works
Influence of Digital Technology on Roadmap Development for Digital Business Transformation
Strutynska, I., Kozbur, H., Dmytrotsa, L., et al. (2019) · International Conference on Advanced Computer Information Technologies (ACIT) · doi:10.1109/acitt.2019.8780056 · Cited by 25 · see citing works
Vesala, J. (2023) · GRURRR. Gewerblicher Rechtsschutz und Urheberrecht, Rechtsprechungs-Report/GRUR-DVD/GRUR-CD/IIC/Gewerblicher Rechtsschutz und Urheberrecht/Gewerblicher Rechtsschutz und Urheberrecht. Internationaler Teil · doi:10.1007/s40319-023-01301-2 · Cited by 24 · see citing works
Copyright and the Progress of Science: Why Text and Data Mining Is Lawful
Carroll, M. W. (2019) · eYLS (Yale Law School) · Cited by 23 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
University Students’ Insights of Generative Artificial Intelligence (AI) Writing Tools
Gasaymeh, A.-M. M., Beirat, M. A., & Abu Qbeita, A. A. (2024) · Education Sciences · doi:10.3390/educsci14101062 · Cited by 74 · see citing works
Rethinking open source generative AI: open washing and the EU AI Act
Liesenfeld, A., & Dingemanse, M. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3659005 · Cited by 67 · see citing works
Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine
Lewin, S., Chetty, R., Ihdayhid, A. R., & Dwivedi, G. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.06.029 · Cited by 36 · see citing works
Lin, Y., Luo, Z., Ye, Z., Zhong, N., Zhao, L., Zhang, L., Li, X., Chen, Z., & Chen, Y. (2025) · JMIR Medical Education · doi:10.2196/71125 · Cited by 31 · see citing works
“At the international level, binding technology-sharing agreements should request that holders of advanced models provide architectural access under fair-use principles, emphasizing the need to balance innovation with equitable access, while emulating open-source paradigms as a reference model [186].”
Ethical framework for responsible foundational models in medical imaging
Jha, D., Durak, G., Das, A., Sanjotra, J., Susladkar, O., Sarkar, S., Rauniyar, A., Kumar Tomar, N., Peng, L., Li, S., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1544501 · Cited by 21 · see citing works
Estimating the quality of academic books from their descriptions with ChatGPT
Thelwall, M., & Cox, A. (2025) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2025.103023 · Cited by 9 · see citing works
- Authors
- Lucchi, N.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Corresponding author
- Lucchi, Nicola
- ORCID
- Lucchi, Nicola
- Venue
- European Journal of Risk Regulation
- Publisher
- Cambridge University Press (CUP)
- Published
- 2023-08-29
- Volume
- 15
- Issue
- 3
- Pages
- 602-624
- ISSN
- 1867-299X (ISSN-L)
- 2190-8249
- OpenAlex
- W4386246120
- Cited by
- 173
- References
- 7
- Citations by year
- 2026: 43 · 2025: 81 · 2024: 48 · 2023: 1
- FWCI
- 16.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Risk Regulation (publishedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-nd)
- Institutional Research Information System University of Ferrara (University of Ferrara) (submittedVersion, cc-by)
- ELARTU (Ternopil National Technical University) (submittedVersion)
- Funders
- U.S. Department of Commerce
- Office of Science
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Political Science and International Relations › Intellectual Property Rights and Media
- Keywords
- ChatGPT, generative AI, copyright, training data, fair use, derivative works, authorship, copyright regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
