DIGITAL ART, ARTIFICIAL INTELLIGENCE, AND COPYRIGHT PROTECTION: CHALLENGES FOR CREATIVE INDUSTRIES
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The expansion of artificial intelligence in digital art has transformed creative production while exposing unresolved weaknesses in copyright doctrine, creative ownership, training-data governance, and institutional accountability. This study examines how automated image generation, platform-based creative tools, and human-machine production challenge established assumptions about authorship, originality, ownership, licensing, disclosure, and ethical responsibility in creative industries. The paper adopts a mixed legal-empirical design. First, it conducts a qualitative doctrinal and comparative analysis supported by coded evidence from a 42-item document corpus comprising scholarly legal studies, institutional policy materials, court and registration materials, and sector guidance documents. Second, it adds an empirical dataset extension using a 120,000-image real/generated archive and a 124,170-record WikiArt metadata corpus to examine authenticity uncertainty and training-data scale. The document-corpus analysis identifies authorship uncertainty and training-data legitimacy as the most persistent governance risks. The empirical extension shows that basic visual features can distinguish real and generated images with moderate accuracy (0.720), AUC (0.799), and F1 score (0.706), but also produces substantial false positives and false negatives. These results indicate that technical detection cannot replace legal documentation of human contribution, platform rights, training-data transparency, and disclosure procedures. The study contributes an integrated legal-ethical and data-informed governance framework for copyright offices, platforms, publishers, cultural institutions, and creative-industry firms. It concludes that copyright modernization should protect human creative labor while establishing clearer standards for commercial clearance, training-data legitimacy, attribution, and responsible platform governance.
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[1] Abbott, R. (2016). I think, therefore I invent: Creative computers and the future of patent law. Boston College Law Review, 57(4), 1079-1126. DOI: https://doi.org/10.2139/ssrn.2727884
[2] Anantrasirichai, N., & Bull, D. (2020). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55, 589-656. DOI: https://doi.org/10.1007/s10462-021-10039-7
[3] Andersen v. Stability AI Ltd., No. 3:23-cv-00201 (N.D. Cal. 2023-2024).
[4] Bonadio, E., & McDonagh, L. (2020). Artificial intelligence as producer and consumer of copyright works: Evaluating the consequences of algorithmic creativity. Intellectual Property Quarterly, 2020(2), 112-137.
[5] Bridy, A. (2016). The evolution of authorship: Work made by code. Columbia Journal of Law & the Arts, 39(3), 395-401.
[6] Copyright Alliance. (2024). Principles for copyright, artificial intelligence, and creator rights. Copyright Alliance.
[7] Deltorn, J. M., & Macrez, F. (2018). Authorship in the age of machine learning and artificial intelligence. Center for International Intellectual Property Studies Research Paper. DOI: https://doi.org/10.2139/ssrn.3261329
[8] Dornis, T. W. (2020). Artificial creativity: Emergent works and the void in current copyright doctrine. Yale Journal of Law & Technology, 22, 1-62. DOI: https://doi.org/10.2139/ssrn.3451480
[9] Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating art by learning about styles and deviating from style norms. Proceedings of the International Conference on Computational Creativity.
[10] European Commission. (2025). Template for general-purpose AI model providers to summarise the data used to train their model. Directorate-General for Communications Networks, Content and Technology. https://digital-strategy.ec.europa.eu/
[11] Geiger, C., Frosio, G., & Bulayenko, O. (2020). Text and data mining in the proposed copyright reform: Making the EU ready for an age of big data? IIC - International Review of Intellectual Property and Competition Law, 51, 814-844. DOI: https://doi.org/10.1007/s40319-018-0722-2
[12] Getty Images (US), Inc. v. Stability AI, Inc., No. 1:23-cv-00135 (D. Del. 2023).
[13] Ginsburg, J. C., & Budiardjo, L. A. (2019). Authors and machines. Berkeley Technology Law Journal, 34(2), 343-448.
[14] Government of the United Kingdom. (2024). Copyright and Artificial Intelligence: Consultation. Department for Science, Innovation and Technology; Department for Culture, Media and Sport; Intellectual Property Office. https://www.gov.uk/government/consultations/copyright-and-artificial-intelligence
[15] Grimmelmann, J. (2016). There's no such thing as a computer-authored work. Columbia Journal of Law & the Arts, 39(3), 403-416.
[16] Guadamuz, A. (2017). Do androids dream of electric copyright? Comparative analysis of originality in artificial intelligence generated works. Intellectual Property Quarterly, 2017(2), 169-186.
[17] Hertzmann, A. (2018). Can computers create art? Arts, 7(2), 18. DOI: https://doi.org/10.3390/arts7020018
[18] Hristov, K. (2017). Artificial intelligence and the copyright dilemma. IDEA: The Journal of the Franklin Pierce Center for Intellectual Property, 57(3), 431-454.
[19] Hugenholtz, P. B., & Quintais, J. P. (2021). Copyright and artificial creation: Does EU copyright law protect AI-assisted output? IIC - International Review of Intellectual Property and Competition Law, 52, 1190-1216. DOI: https://doi.org/10.1007/s40319-021-01115-0
[20] Kop, M. (2020). AI and intellectual property: Towards an articulated public domain. Texas Intellectual Property Law Journal, 28, 297-341. DOI: https://doi.org/10.2139/ssrn.3409715
[21] Lee, E. (2022). Artificial intelligence and the future of copyright. Journal of the Copyright Society of the U.S.A., 69, 1-39.
[22] Lemley, M. A., & Casey, B. (2021). Fair learning. Texas Law Review, 99, 743-785. DOI: https://doi.org/10.2139/ssrn.3528447
[23] Lucchi, N. (2021). The impact of artificial intelligence on copyright law: An analysis of authorship and ownership. Queen Mary Journal of Intellectual Property, 11(4), 407-425.
[24] Margoni, T. (2018). Artificial intelligence, machine learning and EU copyright law: Who owns AI? CREATe Working Paper. DOI: https://doi.org/10.2139/ssrn.3299523
[25] Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. DOI: https://doi.org/10.3390/arts8010026
[26] McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship and intention in computer generated art. In Computational Creativity Research. DOI: https://doi.org/10.1007/978-3-030-16667-0_3
[27] Mezei, P., & Harkai, I. (2022). Machine-created works and copyright: The European perspective. JIPITEC, 13(2), 115-132.
[28] Murray, M. D. (2021). Artificial intelligence and the practice of law: The implications for authorship and copyright. University of Miami Law Review, 75, 1-52.
[29] Ramalho, A. (2017). Will robots rule the artistic world? A proposed model for the legal status of creations by artificial intelligence systems. Journal of Internet Law, 21(1), 1-15. DOI: https://doi.org/10.2139/ssrn.2987757
[30] Ravid, S. Y., & Vélez-Hernández, L. A. (2018). Copyrightability of artworks produced by creative robots and originality: The formality-objective model. Minnesota Journal of Law, Science & Technology, 19, 1-53. DOI: https://doi.org/10.24926/15529541.3793
[31] Rosati, E. (2021). Copyright and artificial intelligence: Challenges and policy responses. GRUR International, 70(8), 741-750.
[32] Sag, M. (2019). The new legal landscape for text mining and machine learning. Journal of the Copyright Society of the U.S.A., 66, 291-367. DOI: https://doi.org/10.2139/ssrn.3331606
[33] Samuelson, P. (2020). Allocating ownership rights in computer-generated works. University of Pittsburgh Law Review, 81, 1185-1221.
[34] Sobel, B. L. W. (2017). Artificial intelligence's fair use crisis. Columbia Journal of Law & the Arts, 41(1), 45-97.
[35] Stokes, S. (2021). Digital copyright: Law and practice in the creative industries. Hart Publishing.
[36] Thaler v. Perlmutter, 687 F. Supp. 3d 140 (D.D.C. 2023), aff’d, No. 23-5233 (D.C. Cir. 2025). https://media.cadc.uscourts.gov/opinions/docs/2025/03/23-5233.pdf
[37] United States Copyright Office. (2023). Zarya of the Dawn: Registration correspondence and cancellation/limitation decision. U.S. Copyright Office.
[38] United States Copyright Office. (2025). Copyright and Artificial Intelligence, Part 2: Copyrightability. U.S. Copyright Office. https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf
[39] United States Copyright Office. (2025). Copyright and Artificial Intelligence, Part 3: Generative AI Training. U.S. Copyright Office. https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report.pdf
[40] United States Copyright Office Review Board. (2023). Second Request for Reconsideration for Refusal to Register Théâtre D’opéra Spatial. U.S. Copyright Office.
[41] World Intellectual Property Organization. (2020). WIPO Conversation on Intellectual Property and Artificial Intelligence: Revised Issues Paper. WIPO.
[42] Yanisky-Ravid, S., & Liu, X. (2017). When artificial intelligence systems produce inventions: An alternative model for patent law at the 3A era. Cardozo Law Review, 39, 2215-226 DOI: https://doi.org/10.2139/ssrn.2931828