SMART CONTRACTS FOR AI-GENERATED ART RIGHTS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6803Keywords:
AI-Generated Art, Smart Contracts, Digital Rights Management, Blockchain Provenance, Copyright and Authorship, TokenizationAbstract [English]
The swift AI-generated art development has further fueled the discussion on both authorship and ownership, as well as on whether digital rights can be enforced. The existing intellectual property paradigms lack the ability to recognise works produced by autonomous systems fully or in part, which presents proxies in the maintenance of copyright, derivatives and cross-jurisdictional identification of AI-related rights. With more and more creative outputs based on algorithmic processes, there is an urgent requirement to have transparent, tamper-resistant processes that would be able to define, assign and protect right at scale. One of the promising infrastructures to facilitate legal and economic aspects of AI-generated art is the use of smart contracts, which are the self-executable agreements that run on blockchain networks. This paper discusses how authorship claims can be encoded in smart contracts, how royalty payments can be automated, and how programmable access controls can be offered, at the same time, offering verifiable provenance by tokenizing the provenance. We analyze technical specifications of creating powerful metadata standards to cover creation parameters, level of contributions, and model lineage. Moreover, we discuss interoperability issues in the heterogeneous blockchains and digital marketplaces, which are limited to the immutability, upgradability, and long-term security. In addition to the technical design, the paper evaluates the ethical impact, such as the fairness to human designers, responsible design of AI innovators, and risks to society in general of bias, exploitation, and its unequal distribution of rights-management systems.
References
Bellini, V., and Bignami, E. G. (2025). Generative Pre-Trained Transformer 4 (GPT-4) in Clinical Settings. The Lancet Digital Health, 7(1), e6–e7. https://doi.org/10.1016/j.landig.2024.12.002 DOI: https://doi.org/10.1016/j.landig.2024.12.002
Bello, O., and Zeadally, S. (2019). Toward Efficient Smartification of the Internet of Things (IoT) Services. Future Generation Computer Systems, 92, 663–673. https://doi.org/10.1016/j.future.2017.09.083 DOI: https://doi.org/10.1016/j.future.2017.09.083
Brynjolfsson, E., Li, D., and Raymond, L. R. (2023). Generative AI at Work (NBER Working Paper No. 31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161 DOI: https://doi.org/10.3386/w31161
Chatterjee, S., and Ramamurthy, B. (2024). Efficacy of Various Large Language Models in Generating Smart Contracts. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-85363-0_31 DOI: https://doi.org/10.1007/978-3-031-85363-0_31
Choi, B. K., Sohn, D. H., and Hong, J. (2025). Analysis and Comparison of PPP ZTD Using Empirical Models GPT, GPT-2, and GPT-3. Journal of Positioning, Navigation, and Timing, 14, 21–28.
Epstein, Z., Hertzmann, A., Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., Leach, N., and Investigators of Human Creativity. (2023). Art and the Science of Generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451 DOI: https://doi.org/10.1126/science.adh4451
Gardazi, N. M., Daud, A., Malik, M. K., Bukhari, A., Alsahfi, T., and Alshemaimri, B. (2025). BERT Applications in Natural Language Processing: A Review. Artificial Intelligence Review, 58, Article 166. https://doi.org/10.1007/s10462-025-11162-5 DOI: https://doi.org/10.1007/s10462-025-11162-5
Khoramnejad, F., and Hossain, E. (2024). Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges. arXiv. https://doi.org/10.1109/COMST.2025.3535554 DOI: https://doi.org/10.1109/COMST.2025.3535554
Kiani, R., and Sheng, V. S. (2024). Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review. Electronics, 13(12), Article 2295. https://doi.org/10.3390/electronics13122295 DOI: https://doi.org/10.3390/electronics13122295
Lai, Z., Wu, T., Fei, X., and Ling, Q. (2024). BERT4ST: Fine-Tuning Pre-Trained Large Language Model for wind Power Forecasting. Energy Conversion and Management, 307, Article 118331. https://doi.org/10.1016/j.enconman.2024.118331 DOI: https://doi.org/10.1016/j.enconman.2024.118331
Matarazzo, A., and Torlone, R. (2025). A Survey on Large Language Models with Some Insights on their Capabilities and Limitations. arXiv. https://arxiv.org/abs/2501.04040
Nguyen, C. T., Liu, Y., Du, H., Hoang, D. T., Niyato, D., Nguyen, D. N., and Mao, S. (2024). Generative AI-Enabled Blockchain Networks: Fundamentals, Applications, and Case Study. IEEE Network, 39(2), 232–241. https://doi.org/10.1109/MNET.2024.3412161 DOI: https://doi.org/10.1109/MNET.2024.3412161
Prathigadapa, S., and Daud, S. B. M. (2025). A Review of Virtual Tutoring Systems and Student Performance Analysis Using GPT-3. Journal of Learning for Development, 12(1), 168–181. https://doi.org/10.56059/jl4d.v12i1.1367 DOI: https://doi.org/10.56059/jl4d.v12i1.1367
Rathore, M. M., Shah, S. A., Awad, A., Shukla, D., Vimal, S., and Paul, A. (2021). A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities. Sustainability, 13(14), Article 7606. https://doi.org/10.3390/su13147606 DOI: https://doi.org/10.3390/su13147606
Zeid, A., Sundaram, S., Moghaddam, M., Kamarthi, S., and Marion, T. (2019). Interoperability in Smart Manufacturing: Research Challenges. Machines, 7(2), Article 21. https://doi.org/10.3390/machines7020021 DOI: https://doi.org/10.3390/machines7020021
Zhao, J., Chen, X., Yang, G., and Shen, Y. (2024). Automatic Smart Contract Comment Generation via Large Language Models and In-Context Learning. Information and Software Technology, 168, Article 107405. https://doi.org/10.1016/j.infsof.2024.107405 DOI: https://doi.org/10.1016/j.infsof.2024.107405
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Yogesh

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.























