
Theory and practice of intellectual property
№ 3 / 2026
ISSN (Print) 2308-0361
ISSN (Online) 2519-2744
DOI: https://doi.org/10.33731/tpip.2026.3.10
Submitted 2026-07-30
Accepted 2026-08-14
Published 2026-09-10

CONTU’s doctrinal errors and their privacy implications: from copyright law to personal data protection in AI regulation
Volodymyr Karpunets
Assistant at the Department of Law, Public Management and Administration,
Zhytomyr Polytechnic State University,
PhD Student at the, Department of Private and Public Law,
Kyiv National University of Technologies and Design
Kyiv, Ukraine
https://orcid.org/0009-0000-0311-495X
Abstract
This article critically analyses the doctrinal origins of the contemporary crisis in AI regulation. Examining the U.S. National Commission on New Technological Uses of Copyrighted Works (CONTU, 1974–1978), it argues that current attempts to apply copyright law to AI-generated outputs replicate CONTU’s error — treating utilitarian objects as literary works. Particular attention is paid to the privacy dimension: mass training of generative models on LAION-5B and Common Crawl datasets violates the General Data Protection Regulation (GDPR) and renders data subject rights unexercisable. Drawing on contemporary scholarship, the article shows that the anthropocentric copyright model and GDPR instruments (text and data mining (TDM) exceptions, fair use, “legitimate interest”) are structurally unsuitable for generative AI. The author proposes a sui generis framework separating human creativity from technical generation while integrating privacy by design, mandatory Data Protection Impact Assessment (DPIA), and AI content labelling.
Keywords: sui generis legal model, originality criterion, artificial intelligence, utilitarian code, datasets, data protection
References
Bersh, L. (2024). An exceptional formality under Berne: Evasion of copyright protection via the EU’s text and data mining exception. Harvard Journal of Law & Technology, 38(1), 337–368.
https://jolt.law.harvard.edu/assets/articlePDFs/v38/6-Bersh.pdf
Con Díaz, G. (2016). The text in the machine: American copyright law and the many natures of software, 1974–1978. Technology and Culture, 57(4), 753–779.
https://doi.org/10.1353/tech.2016.0106
Geller, P. E. (2000). Copyright history and the future: What’s culture got to do with it? Journal of the Copyright Society of the USA, 47, 209–264.
Ginsburg, J. C. (2018). People not machines: Authorship and what it means in the Berne Convention. IIC – International Review of Intellectual Property and Competition Law, 49, 131–135.
https://doi.org/10.1007/s40319-018-0683-7
Hughes, J. (1988). The philosophy of intellectual property. Georgetown Law Journal, 77, 290–330.
http://justinhughes.net/writings-files/a-HUGHES%20Philosophy%20of%20IP.pdf
Kokhanovska, O. V. (2006). Theoretical problems of information relations in civil law. Kyiv University Press.
Kretschmer, M., Margoni, T., & Oruc, P. (2024). Copyright law and the lifecycle of machine learning models. IIC – International Review of Intellectual Property and Competition Law, 55(1), 110–138.
https://doi.org/10.1007/s40319-023-01419-3
Kuru, T. (2024). Lawfulness of the mass processing of publicly accessible online data to train large language models. International Data Privacy Law, 14(4), 326–351.
https://doi.org/10.1093/idpl/ipae013
Locke, J. (1980). Second treatise of government (C. B. Macpherson, Ed.). Hackett Publishing Company. (Original work published 1689).
https://www.earlymoderntexts.com/assets/pdfs/locke1689b.pdf
Novelli, C., Casolari, F., Hacker, P., Spedicato, G., & Floridi, L. (2024). Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity. Computer Law & Security Review, 55, Article 106066.
https://doi.org/10.1016/j.clsr.2024.106066
Ruschemeier, H. (2025). Generative AI and data protection. Cambridge Forum on AI: Law and Governance, 1, Article e6.
https://doi.org/10.1017/cfl.2024.2
Samuelson, P. (1984). CONTU revisited: The case against copyright protection for computer programs in machine-readable form. Duke Law Journal, 4, 665–769.
https://scholarship.law.duke.edu/cgi/viewcontent.cgi?article=2884&context=dlj
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., & Jitsev, J. (2022). LAION-5B: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35.
https://arxiv.org/abs/2210.08402
Shtefan, A. (2026). Form of expression of a computer program in the EU legislation and in the case law of the European Court of Justice. Bulletin of Lviv Polytechnic National University. Series: Legal Sciences, 13(1), pp. 443−451.
https://doi.org/10.23939/law2026.49.443
Shtefan, O. (2006). The concept of the object of copyright and the criteria for its protectability. Theory and practice of intellectual property, 6, 3–8.
Thiel, D. (2023). Identifying and eliminating CSAM in generative ML training data and models. Stanford Digital Repository.
https://purl.stanford.edu/kh752sm9123
Van Bekkum, M., & Zuiderveen Borgesius, F. (2023). Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception? Computer Law & Security Review, 48, Article 105770.
https://doi.org/10.1016/j.clsr.2022.105770
Yakubivskyi, I. Ye. (2024). Civil-law regime of objects generated by artificial intelligence. Uzhhorod National University Herald. Series: Law, 82, 355–361.
https://doi.org/10.24144/2307-3322.2024.82.1.56