با وجود تمرکز بخش قابل توجهی از مطالعات مرتبط با هوش مصنوعی بر ابعاد فناورانه، اخلاقی و تنظیمگری هوش مصنوعی، نقش دادههای بومی بهعنوان مزیت نسبی کشورها کمتر مورد توجه قرار گرفته است. هدف این پژوهش، شناسایی الزامات حکمرانی داده در توسعه هوش مصنوعی با تأکید بر بهرهبرداری از ظرفیت دادههای بومی ایران است. این پژوهش با رویکرد کیفی و بر اساس مصاحبههای نیمهساختاریافته با خبرگان حوزه هوش مصنوعی و سیاستگذاری انجام شده و دادهها با استفاده از تحلیل مضمون بررسی شدهاند. یافتهها نشان داد که هشت مؤلفه شامل تقویت اعتماد عمومی به زیستبوم داده، ایجاد زیرساختهای تنظیمگری داده، توسعه نظامهای مالکیت داده، تقویت امنیت داده، استانداردسازی داده، افزایش دسترسیپذیری داده، انباشت و ذخیرهسازی داده و گمنامسازی و ناشناسسازی دادهها از مهمترین الزامات حکمرانی داده در توسعه هوش مصنوعی ایران به شمار میروند. نتایج پژوهش بیانگر آن است که دادههای بومی میتوانند بهعنوان یک دارایی راهبردی و مزیت رقابتی کشور در توسعه هوش مصنوعی ایفای نقش کنند، مشروط بر آنکه حکمرانی داده بهصورت یکپارچه و مبتنی بر چارچوبهای حقوقی، فنی و نهادی مناسب استقرار یابد.
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.
Anderson, K. P. (2019). Artificial intelligence‐augmented ECG assessment: The promise and the challenge. Journal of Cardiovascular Electrophysiology, 30(5).
(2025). A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation. https://arxiv.org
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
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
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).
Corbin, J., & Strauss, A. (2015). Basics of Qualitative Research (4th ed.). Sage.
Council on Business & Society Insights. (2025). Data commons, the new frontier. Council on Business & Society Insights
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.
(2025). ETSI work programme for trustworthy data ecosystems. European Telecommunications Standards Institute. https://www.etsi.org
FAIR EDIH. (2025). Findata and Luxembourg forge data governance alliance ahead of EU health data reforms. FAIR EDIH News. https://www.fairedih.eu
Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. 1(1)
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.
Hartung, T. (2025). Data resilience and scientific sovereignty: An interview with Professor Thomas Hartung. https://www.frontiersin.org/news/2025/10/02
Holmström Jonny, Magnusson Johan. (2025). Balancing innovation and regulation: The Data Sovereignty Assessment Framework. The Journal of Strategic Information Systems. In press
Hummel, P., Braun, M., Augsberg, S., & Dabrock, P. (2018). Sovereignty and data sharing. ITU Journal on Future and Evolving Technologies, 1(2).
(2025). Global Symposium for Regulators 2025 - Chairman's report. International Telecommunication Union. https://www.itu.int
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.
Janssen, M., et al. (2020). Data Governance: Organizing Data for Trustworthy AI. Government Information Quarterly, Volume 37, Issue 3,
Judiciary Statistics Center [In Persian].
Karolinska Institutet. (2025). Slutrapport KI Data Repository – Fas 1 Flaggskeppsprojekt inom Svensk Nationell Datatjänst. Zenodo. https://zenodo.org
Khatri, V., & Brown, C. V. (2010). Designing Data Governance. Communications of the ACM
Nambu, T. (2016). Legal regulations and public policies for next-generation robots in Japan. Ai & society, 31(4), 483-500.
(2024). OECD Framework for the Classification of AI Systems.
Panian, Z., (2010) Some Practical Experiences in Data Governance, World Academy of Science, Engineering and Technology.
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].
Qiu, Yanrui & Hu, Zhimin. 2026. Construction of a theoretical Framework for Scientific DataGovernance. Scientific Data
Rivas, H., & Wac, K. (Eds.). (2018). Digital health: scaling healthcare to the world. Springer.
Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Schwarzfuchs, E. N. (2025). FAIR data, fair data: data management principles and data ownership in citizen science. Information, Communication & Society, 1–17
Siau, K. (2017). Impact of artificial intelligence, robotics, and automation on higher education. AMCIS.
Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage
Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society, 40(2), 137-157.
Thangaraju, D. (2022). Data Classification: Enabling Robust Data Governance and Access Management in Enterprise Environments. International Journal of Innovative Research in Computer Technology (IJIRCT)
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
Thomson Reuters Institute. (2025). Outgrowing KIS: Why AI needs a new SMART data rulebook. Thomson Reuters Institute. https://www.thomsonreuters.com
S. Federal CDO Council & CISO Council. (2025). Federal Zero Trust Data Security Guide. Data.gov. https://www.data.gov
(2021). Recommendation on the Ethics of Artificial Intelligence
United Nations (2023). The Global Digital Compact. United Nations Office for Digital and Emerging Technologies
University of Évora. (2025). Alentejo Research Data Centre (RDC): Infrastructure for research data storage and access. University of Évora. https://www.uevora.pt
Wirtz, B. W., & Müller, W. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7), 1076-1100.
World Bank (2021). World Development Report 2021: Data for Better Lives.
