Requirements for Artificial Intelligence Policymaking with an Emphasis on the Comparative Advantage of Data in Iran

Document Type : Research Article

Authors

1 Master of Public Policy, Faculty of Law & Political Science, Allameh Tabatabai University, Tehran, Iran

2 Assistant Professor of Public Policy, Faculty of Law & Political Science, Allameh Tabataba'i University, Tehran, Iran

3 Associate Professor of Public Policy, Faculty of Law & Political Science, University of Tehran, Tehran, Iran

10.22059/jppolicy.2026.107845

Abstract

As a transformative technology, artificial intelligence has significant potential to improve governance, public services, and economic development. However, its effective use depends on efficient data policymaking. In Iran, the relative advantage in accessing indigenous data—especially legal, health, and public service data—has provided an unparalleled opportunity to develop proprietary algorithms. This qualitative study, based on document analysis and expert interviews, shows that the main problem in the country is not a lack of data, but rather a weakness in data governance, a lack of standardization, and a lack of legal and institutional mechanisms for the release, exchange, and protection of data. The experience of leading countries shows that “data policy” is the main foundation for the development of artificial intelligence, and without it, technological investments will not achieve the desired results. The research findings indicate the need to establish national data infrastructures, anonymize and standardize sensitive data, strengthen public trust, and design legal frameworks for data ownership and exchange. The main conclusion is that data governance should be placed as a policy priority on the Iranian governance agenda. Achieving this goal requires the government to play an active role in establishing national data centers, supporting data-driven businesses, and developing transparent regulations to balance public interest, privacy, and technological development.

Keywords

References

  1. Al Marri, A., Albloosh, F., Moussa, S., & Elmessiry, H. (2019). Study on the impact of artificial intelligence on government E-service in Dubai. In 2019 International Conference on Digitization (ICD) (pp. 153-159). IEEE.
  2. Anderson, K. P. (2019). Artificial intelligence‐augmented ECG assessment: The promise and the challenge. Journal of Cardiovascular Electrophysiology, 30(5).
  3. (2025). A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation. https://arxiv.org
  4. Baraku, V., Paraskakis, I., Veloudis, S., & Yadav, P. (2024). Responsible Information Sharing in the Era of Big Data Analytics: Facilitating Digital Economy Through the Use of Blockchain Technology and Observing GDPR. Proceedings of the 14th International Conference on Cloud Computing and Services Science (CLOSER), pp. 257–264
  5. Behzadifar, Meysam. Azari, Samad, Sajedimehr, Negin (2025). Challenges of using artificial intelligence in Iran's health system: a qualitative study. J PREV MED HYG 2025; 66
  6. Cave, S., & ÓhÉigeartaigh, S. S. (2018). An AI race for strategic advantage: rhetoric and risks. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 36-40).
  7. Corbin, J., & Strauss, A. (2015). Basics of Qualitative Research (4th ed.). Sage.
  8. Council on Business & Society Insights. (2025). Data commons, the new frontier. Council on Business & Society Insights
  9. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.
  10. (2025). ETSI work programme for trustworthy data ecosystems. European Telecommunications Standards Institute. https://www.etsi.org
  11. FAIR EDIH. (2025). Findata and Luxembourg forge data governance alliance ahead of EU health data reforms. FAIR EDIH News. https://www.fairedih.eu
  12. Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. 1(1)
  13. Guenduez, A. A., & Mettler, T. (2023). Strategically constructed narratives on artificial intelligence: What stories are told in governmental artificial intelligence policies?. Government Information Quarterly, 40(1), 101719.
  14. Hartung, T. (2025). Data resilience and scientific sovereignty: An interview with Professor Thomas Hartung. https://www.frontiersin.org/news/2025/10/02
  15. Holmström Jonny, Magnusson Johan. (2025). Balancing innovation and regulation: The Data Sovereignty Assessment Framework. The Journal of Strategic Information Systems. In press
  16. Hummel, P., Braun, M., Augsberg, S., & Dabrock, P. (2018). Sovereignty and data sharing. ITU Journal on Future and Evolving Technologies, 1(2).
  17. (2025). Global Symposium for Regulators 2025 - Chairman's report. International Telecommunication Union. https://www.itu.int
  18. Janssen, M., & Kuk, G. (2016). Big and open linked data (BOLD) in research, policy, and practice. Journal of Organizational Computing and Electronic Commerce, 26(1-2), 3-13.
  19. Janssen, M., et al. (2020). Data Governance: Organizing Data for Trustworthy AI. Government Information Quarterly, Volume 37, Issue 3,
  20. Judiciary Statistics Center [In Persian].
  21. Karolinska Institutet. (2025). Slutrapport KI Data Repository – Fas 1 Flaggskeppsprojekt inom Svensk Nationell Datatjänst. Zenodo. https://zenodo.org
  22. Khatri, V., & Brown, C. V. (2010). Designing Data Governance. Communications of the ACM
  23. Nambu, T. (2016). Legal regulations and public policies for next-generation robots in Japan. Ai & society, 31(4), 483-500.
  24. (2024). OECD Framework for the Classification of AI Systems.
  25. Panian, Z., (2010) Some Practical Experiences in Data Governance, World Academy of Science, Engineering and Technology.
  26. Pour Ezzat, A. A; Abdi, B. (2019). Identifying Meta-process of Information and Communication Technology Trends in Defense Industry Future Planning Towards Achieving an Islamic-Iranian Model of Progress, Defense Futures Studies Quarterly, 3(10) [In Persian].
  27. Qiu, Yanrui & Hu, Zhimin. 2026. Construction of a theoretical Framework for Scientific DataGovernance. Scientific Data
  28. Rivas, H., & Wac, K. (Eds.). (2018). Digital health: scaling healthcare to the world. Springer.
  29. Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  30. Schwarzfuchs, E. N. (2025). FAIR data, fair data: data management principles and data ownership in citizen science. Information, Communication & Society, 1–17
  31. Siau, K. (2017). Impact of artificial intelligence, robotics, and automation on higher education. AMCIS.
  32. Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage
  33. Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society, 40(2), 137-157.
  34. Thangaraju, D. (2022). Data Classification: Enabling Robust Data Governance and Access Management in Enterprise Environments. International Journal of Innovative Research in Computer Technology (IJIRCT)
  35. The Alan Turing Institute. (2025). AI will be key to future national security decision making but brings its own risks The Alan Turing Institute. https://www.turing.ac.uk
  36. Thomson Reuters Institute. (2025). Outgrowing KIS: Why AI needs a new SMART data rulebook. Thomson Reuters Institute. https://www.thomsonreuters.com
  37. S. Federal CDO Council & CISO Council. (2025). Federal Zero Trust Data Security Guide. Data.gov. https://www.data.gov
  38. (2021). Recommendation on the Ethics of Artificial Intelligence
  39. United Nations (2023). The Global Digital Compact. United Nations Office for Digital and Emerging Technologies
  40. University of Évora. (2025). Alentejo Research Data Centre (RDC): Infrastructure for research data storage and access. University of Évora. https://www.uevora.pt
  41. Wirtz, B. W., & Müller, W. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7), 1076-1100.
  42. World Bank (2021). World Development Report 2021: Data for Better Lives.
Iranian Journal of Public Policy
Volume 12, Issue 2
June 2026
Pages 369-384

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