Artificial Intelligence and Copyright Policymaking for Cinematic Works with an Emphasis on the Role of Collective Management Organizations

Document Type : Research Article

Authors

1 Ph.D. Student of Private Law, Faculty of Law and Political Sciences, Islamic Azad University of Bushehr, Bushehr, Iran

2 Associaate Professor of Private Law, Faculty of Law and Political Sciences, University of Tehran, Tehran, Iran

3 Professor of Private Law, Faculty of Law and Political Sciences, University of Tehran, Tehran, Iran

10.22059/jppolicy.2026.106374

Abstract

Today, artificial intelligence has played a role in the pre-production process to the release and in the management of intellectual property rights of cinematic works. Artificial intelligence can also help in monitoring the infringement of copyrights of cinematic works. These important aspects of the interaction of artificial intelligence and intellectual property related to cinematic works lead us to look into the issue of how artificial intelligence can participate in the management of rights of cinematic works? The present article uses an analytical-descriptive method and finally concludes that the possibility of copyright infringement in the process of creating works based on artificial intelligence is serious; therefore, collective management organizations can, as an active actor in future policymaking, monitor the market, issue exploitation licenses to train systems based on machine learning, and receive contractual compensation.

Keywords

References

  1. Adelsbach A, K. S., Sadeghi A-R. . (2002.). Cryptography Meets Watermarking: Detecting Watermarks with Minimal or Zero Knowledge Disclosure. . Zenodo
  2. Agarwal, S., & Prasad, B. R. (2015, 18-20 Dec. 2015). High speed streaming data analysis of web generated log streams. 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS),
  3. Akinrinola, O., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E. (2024). Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability. GSC Advanced Research and Reviews, 18(3), 050-058.
  4. Al-Shareef, Y. (2025). CHRExpert: An AI-Driven Court of Human Rights Expert Assistant for Legal Practitioners Utilizing Transformer Models. IEEE Access, 13, 41097-41110. https://doi.org/10.1109/ACCESS.2025.3547763
  5. Anacleto Correia, P. B. Á. (2024). Harnessing artificial intelligence for enhanced environmental, social, and governance reporting: A new paradigm in corporate transparency. corporate governance, research and advanced practices. https://doi.org/10.22495/cgrapp15
  6. AudreyM, P. H. (2024). NYT v. OpenAI: The Times’s About-Face. HTTPS://HARVARDLAWREVIEW.ORG/BLOG/2024/04/NYT-V-OPENAI-THE-TIMESS-ABOUT-FACE/
  7. Bansal, K., Sabo, T., & Bansal, E. M. (2016). Data Clustering and Visualization based various Machine learning techniques.
  8. Benramdane, M. K., Kornyshova, E., Bouzefrane, S., & Maupas, H. (2024). Supervised Machine Learning for Matchmaking in Digital Business Ecosystems and Platforms. Information Systems Frontiers, 26(4), 1331-1343. https://doi.org/10.1007/s10796-022-10357-3
  9. Bernard Owusu Antwi, B. O. A., Augustine Obinna Eziefule. (2024). Transforming Financial Reporting with AI: Enhancing Accuracy and Timeliness. International Journal of Advanced Economics, 6(6). https://doi.org/10.51594/ijae.v6i6.1229
  10. Brittain, B. (2025). Judge explains order for New York Times in OpenAI copyright case, April 5, 2025. https://www.reuters.com/legal/litigation/judge-explains-order-new-york-times-openai-copyright-case-2025-04-04/
  11. Carroll, M. W. (2019). Copyright and the progress of science: why text and data mining is lawful. UC Davis L. Rev., 53, 893.
  12. (2024). Collective copyright management in the age of AI. https://news.cgtn.com/news/2024-12-22/Collective-copyright-management-in-the-age-of-AI-1zwkQObbUNG/index.html
  13. Chen, J., DeWitt, D. J., Tian, F., & Wang, Y. (2000). NiagaraCQ: a scalable continuous query system for Internet databases Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Dallas, Texas, USA. https://doi.org/10.1145/342009.335432
  14. Chen, M., Hu, X., Qi, Y., & Masi, D. (2024). AI-driven dynamic pricing for high-value assets in manufacturing and services: optimizing finite horizon sales with demand sensitivity. International Journal of Production Research, 1-13.
  15. Choi, H. W., Qureshi, N. M. F., & Shin, D. R. (2019, 17-20 Feb. 2019). Analysis of Electricity Consumption at Home Using K-means Clustering Algorithm. 2019 21st International Conference on Advanced Communication Technology (ICACT),
  16. Dhir, R., & Raj, A. (2018, 15-17 Dec. 2018). Movie Success Prediction using Machine Learning Algorithms and their Comparison. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC),
  17. Dong, X., Li, W., Le, Y., Jiang, Z., Zhong, J., & Wang, Z. (2025). TermDiffuSum: A Term-guided Diffusion Model for Extractive Summarization of Legal Documents. International Conference on Computational Linguistics,
  18. Ezekiel Onyekachukwu Udeh, P. A., Kudirat Bukola Adeusi, Anwulika Ogechukwu Scott. (2024). AI-Enhanced Fintech communication: Leveraging Chatbots and NLP for efficient banking support. International Journal of Management & Entrepreneurship Research, 6(6). https://doi.org/10.51594/ijmer.v6i6.1164
  19. Farinde, O. (2025). Integrating predictive analytics, machine learning, and scenario-based forecasting for precision-driven budget planning and resource optimization. World Journal of Advanced Research and Reviews, 25(3), 658-677. https://doi.org/10.30574/wjarr.2025.25.3.0777
  20. Geiger, C. (2024). Elaborating a Human Rights-Friendly Copyright Framework for Generative AI. IIC-International Review of Intellectual Property and Competition Law, 55(7), 1129-1165.
  21. Genovesi, S., Haimerl, M., Merget, I., Prange, S. M., Obert, , Wolf, S., & Ziehn, J. (2025). Evaluating Dimensions of AI Transparency: A Comparative Study of Standards, Guidelines, and the EU AI Act.
  22. Gupta, S. K., Garg, T., Raj, S., & Singh, S. (2024, 20-21 Dec. 2024). Box Office Revenue Prediction Using Linear Regression in ML. 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA),
  23. Halima Oluwabunmi Bello , C. I. a. T. V. I. (2024). Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention. World Journal of Advanced Research and Reviews, 23, 68-56. https://doi.org/10.30574/wjarr.2024.23.1.1985
  24. Hamdani, R. E., Mustapha, M., Amariles, D. R., Troussel, A., Meeùs, S., & Krasnashchok, K. (2021). A combined rule-based and machine learning approach for automated GDPR compliance checking Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, São Paulo, Brazil. https://doi.org/10.1145/3462757.3466081
  25. Hammond, K. (2015). Practical Artificial Intelligence for Dummies. In: John Wiley & Sons, Inc.
  26. Josephine Nwadinma Okonkwo, O. A. (2024). Predictive Analytics Techniques for Forecasting Financial Trends and Optimizing Business Processes. international Journal of research and scientific innovation. https://doi.org/10.47772/IJRISS.2024.8110211
  27. Karunakaran, S., Hemasundari, M., Suguna, R., & Thandauthapani, A. (2024, 8-9 Oct. 2024). Integrating AI and ML for Dynamic Pricing Strategies: Innovations in Marketing Analytics and Revenue Management. 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS),
  28. Kirovski, D., Malvar, H., & Yacobi, Y. (2002). Multimedia content screening using a dual watermarking and fingerprinting system Proceedings of the tenth ACM international conference on Multimedia, Juan-les-Pins, France. https://doi.org/10.1145/641007.641086
  29. Lestarini, D., & Raflesia, S. P. (2024, 11-12 Dec. 2024). Towards AI Chatbot Effectiveness: Crucial Elements Impacting User Engagement and 2024 International Conference of Adisutjipto on Aerospace Electrical Engineering and Informatics (ICAAEEI),
  30. Lex, E., Kowald, D., & Schedl, M. (2020). Modeling Popularity and Temporal Drift of Music Genre Preferences. Transactions of the International Society for Music Information Retrieval. https://doi.org/10.5334/tismir.39
  31. Luckham, D. (2008, 2008//). The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Rule Representation, Interchange and Reasoning on the Web, Berlin, Heidelberg.
  32. Ma, M., & Ji, D. (2024, 18-19 Oct. 2024). Dynamic Decision Prediction Model based on GMARIMA and ETA Models. 2024 First International Conference on Software, Systems and Information Technology (SSITCON),
  33. Mallikarjuna Nandi, K. M., E susmitha, Kavya sri Polampalli. (2025). Transformative Applications of Data Science and Machine Learning: Inno-vations in Healthcare, Entertainment and Personal Finance. International Research Journal on Advanced Engineering and Management (IRJAEM), 3(2). https://doi.org/10.47392/IRJAEM.2025.0046
  34. Mehrotra, R., Xue, N., & Lalmas, M. (2020). Bandit based Optimization of Multiple Objectives on a Music Streaming Platform Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA. https://doi.org/10.1145/3394486.3403374
  35. Miernicki, M. (2017). Collective Management of Copyrights between Competition, Regulation, and Monopolism.
  36. Milosevic, Z., Chen, W., Berry, A., & Rabhi, F. A. (2016). Chapter 2 - Real-Time Analytics. In R. Buyya, R. N. Calheiros, & A. V. Dastjerdi (Eds.), Big Data (pp. 39-61). Morgan Kaufmann. https://doi.org/https://doi.org/10.1016/B978-0-12-805394-2.00002-7
  37. Mujtaba, N., & Yuille, A. AI-Powered Financial Services: Enhancing Fraud Detection and Risk Assessment with Predictive Analytics.
  38. office, u. s. c. (2025). copyright and artificial intelligence part 3: generative AI training.
  39. Pathak, R. K. (2024). The Evolution of Film Genres: A Historical Analysis. international journal for research publication and seminar, 15(1). https://doi.org/10.36676/jrps.v15.i1.07
  40. Ponce del Castillo, A. (2025). The role of collective agreements in times of uncertain AI governance: lessons from the Hollywood scriptwriters’ agreement. AI & society, 40(2), 1119-1120.
  41. Pourmohamadimahounaki, S. (2015). Types of Collective Management Organizations [MLJ, 8(0), 11. http://ijmedicallaw.ir/article-1-411-fa.html[in persion]
  42. Qiang, C., & Huang, T. S. (2000, 30 July-2 Aug. 2000). Blind digital watermarking for images and videos and performance analysis. 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532),
  43. Qu, S., Lei, X., Kumar, M. S., S., C. F. T., Jianli, Z., & and Arisian, S. (2022). Matchmaking in reward-based crowdfunding platforms: a hybrid machine learning approach. International Journal of Production Research, 60(24), 7551-7571. https://doi.org/10.1080/00207543.22121870
  44. Raju, U. S. N., Chaitanya, B., Kumar, K. P., Krishna, P. N., & Mishra, P. (2016, 20-22 April 2016). Video Copy Detection in Distributed Environment. 2016 IEEE Second International Conference on Multimedia Big Data (BigMM),
  45. Ravisankar, P. (2025). AI-Powered Fraud Detection and Risk Management in FinTech: Safeguarding Transactions with Machine Learning. International journal of scientific research in computer science, engineering and information technology, 11(1). https://doi.org/10.32628/CSEIT251112241
  46. Ravish Tillu, B. K. K., Vathsala Periyasamy. (2023). Navigating Regulatory Complexity: Leveraging AI/ML for Accurate Reporting. Innovative Pedagogical Approaches and Technological Integration in Modern Education 2(2). https://doi.org/10.60087/vol2.n2.p160
  47. Rosati, E. (2025). The future of the movie industry in the wake of generative AI: A perspective under EU and UK copyright law. Computer Law & Security Review, 59, 106207. https://doi.org/https://doi.org/10.1016/j.clsr.2025.106207
  48. Rudolf Leška, L., Rudolf. (2020). Digital Peripheries Springer Series in Media Industries. https://doi.org/ 1007/978-3-030-44850-9_16
  49. Rupasinghe, M. (2025). The Legal and Ethical Implications of Artificial Intelligence in Europe: Challenges for Intellectual Property Law and Regulation in Musical Industry in Sweden 2024.
  50. Sadeghi Mohsen, M. H. (1395). Collective management organizations of literary and artistic property rights. Legal studies, 3. [in persion]
  51. Sahu, S., Kumar, R., Pathan, M. S., Shafi, J., Kumar, Y., & Ijaz, M. F. (2022). Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System. IEEE Access, 10, 42044-42060. https://doi.org/10.1109/ACCESS.2022.3168161
  52. Saleiro, P., Kuester, B., Stevens, A., Anisfeld, A., Hinkson, L., London, J., & Ghani, R. (2018). Aequitas: A Bias and Fairness Audit Toolkit. ArXiv, abs/1811.05577.
  53. Sao, A., Verma, R., Lokannadha, I., N, S., Mary, S. S. C., & Raj, I. (2025, 24-25 Jan. 2025). Predictive Analytics for Stock Price Forecasting: Machine Learning Techniques in Financial Markets. 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN),
  54. (2024). 5 Key Roles of Collective Management Organizations. https://www.scoredetect.com/blog/posts/5-key-roles-of-collective-management-organizations
  55. Shakeri, Z. (2025). The Literary and Artistic Property Rights System in the Age of Artificial Intelligence; Considerations for Policymaking in Future Governance. Iranian Journal of Public Policy, 11(1), 41-55. https://org/10.22059/jppolicy.2025.101189 [in persion]
  56. Shakeri, Z., & Mohamadi, H. (2015). A Review of the Legal Status of Collective Management Organizations in Today’s World. MLJ, 8(0), 29. http://ijmedicallaw.ir/article-1-412-fa.html[in persion]
  57. Shakeri, Z., & Nourali, S. (2020). Film Makers Collective Management Organization, Investigation in to the legal system of United States, England, France and India. mdrsjrns, 24(3), 89-126. http://clr.modares.ac.ir/article-20-27324-en.html [in persion]
  58. (2025). AI System to Summarize and Analyze Legal Documents: A Transformative Approach. International Scientific Journal of Engineering and Management.
  59. Shao, W., Wang, Z., Wang, X., Qiu, K., Jia, C., & Jiang, C. (2020). LSC: Online auto-update smart contracts for fortifying blockchain-based log systems. Information Sciences, 512, 506-517. https://doi.org/https://doi.org/10.1016/j.ins.2019.09.073
  60. Sharma, A., & Wehrheim, H. (2019, 22-27 April 2019). Testing Machine Learning Algorithms for Balanced Data Usage. 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST),
  61. Sheikh, A., Simske, S. J., & Chong, E. K. P. (2024). Evaluating Artificial Intelligence Models for Resource Allocation in Circular Economy Digital Marketplace. Sustainability, 16(23).
  62. Sood, P., Tanwar, H., Singh, J., Ruhela, A. K., Gupta, N., & Kumar, R. (2024, 21-23 Nov. 2024). Revolutionizing Customer Service: An AI-powered Chatbot Approach using Advanced NLP Techniques. 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON),
  63. Theobald, O. (2021). Machine learning for absolute beginners: a plain English introduction. Scatterplot press.
  64. Verma, S., Paliwal, N., Yadav, K., & Vashist, P. C. (2024, 15-16 March 2024). Ethical Considerations of Bias and Fairness in AI Models. 2024 2nd International Conference on Disruptive Technologies (ICDT),
  65. West, T. (2024). AI and copyright: the collective solution, https://www.bookbrunch.co.uk/page/free-article/ai-and-copyright-the-collective-solution/
Iranian Journal of Public Policy
Volume 12, Issue 1
March 2026
Pages 108-123

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