Designing a Policy Package for Developing Artificial Intelligence (AI) in Iran

Document Type : Research Article

Authors

1 MSc in IT Management, College of Farabi, University of Tehran, Qom, Iran

2 Assistant Professor of Management, College of Farabi, University of Tehran, Qom, Iran

3 Associate Professor of Management, University of Tehran, Tehran, Iran

Abstract

The increasing use of artificial intelligence (AI) in various aspects of human life has forced policymakers in different countries to adopt appropriate policies to increase the advantages of this technology and deal with its development challenges. By examining 18 national documents published by seven countries, the United States, England, China, Australia, Germany, Australia and India, as well as seven international documents published by the European Commission and the OECD in the field of AI policy between the years 2017-2021 and interviews with 15 experts engaged in different sectors of AI policymaking, this research has sought to design a suitable policy package for the development of AI in Iran. By using thematic analysis methods and the fuzzy MCDM model, this research has tried to discover the most important policy objectives and their prioritization, as well as the prioritization of policy instruments to achieve each of these goals. The results show that the most important goals of AI development in the country, in order of priority, are: 1) Economic Growth, 2) Enhancing human capital, 3) Improving infrastructure, 4) Increasing social welfare and improving public services, and 5) Improving research capacities. Also, the most important policy instruments to achieve the discussed policy objectives, in order of priority, are: 1) Regulation and Standardization, 2) R&D Financing, 3) Culture making and education, 4) Networking and cooperation support, 5) Stimulating market demand, 6) Government procurement and 7) Consulting and acceleration. Identifying the most important policy objectives and instruments for the development of AI in the country and their priority will help policymakers make appropriate decisions for the balanced development of the innovation ecosystem of AI in the country.

Keywords


  1. Ali Ahmadi, A., Ghazinoori, S. (2008). Prioritizing policy instruments for supporting new technology-based firms in Iran, using a fuzzy MCDM model. Journal of Science and Technology Policy, 1(3), 73-79. {In Persian}.
  2. Alizadeh, P., Ghazinoory, S., Amiri, M., Ghazinoori, S. (2018). Designing a Policy Mix to Enhance the Business Expenditure on Research and Development (R&D) in Iran. Journal of Management Improvement, 12(3), 1-24. {In Persian}.
  3. Alizadeh, P., Malekifar, F. (2019). Policy Mixes for Science, Technology, and Innovation. Journal of Science and Technology Policy, 12(2), 513-526. {In Persian}.
  4. Amiri, S., Nikkam, N., Sahebinejad, M. (2008). Statistical Survey of Nanotechnology related Patents as an Indicator of Nanotechnology Creation. Journal of Science and Technology Policy, 1(3), 1-13. {In Persian}.
  5. Attride-Stirling, J. (2001). Thematic Networks: An Analytic Tool for Qualitative Qualitative Research, Vol. 1, No. 3, Pp. 385-405.
  6. Bikar, V., Carpon, H., & Cincera, M. (2004). An integrated evaluation scheme of innovation systems from an institutional perspective.
  7. Borrás, S., & Edquist, C. (2013). The choice of innovation policy instruments. Technological forecasting and social change, 80(8), 1513-1522.
  8. Bostrom, N., Dafoe, A., & Flynn, C. (2019). Policy Desiderata for Superintelligent AI. Oxford (Version 4.3, 2018).
  9. Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, Vol. 3, No. 2, Pp. 77-101.
  10. Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., ... & Anderljung, M. (2020). Toward trustworthy AI development. arXiv preprint arXiv:2004.07213. ‏
  11. Calo, R. (2017). Artificial Intelligence policy: a primer and roadmap. UCDL Rev., 51, 399. ‏
  12. Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. EJOR.
  13. Chen, J. F., Hsieh, H. N., & Do, Q. H. (2015). Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Applied Soft Computing, 28, 100-108.
  14. Clark, J., & Guy, K. (1997). Innovation and competitiveness. Technopolis.
  15. Cunningham, P., & Ramlogan, R. (2016). The impact of innovation networks. Handbook of Innovation Policy Impact; 279-317. Edward Elgar Publishing.
  16. Dafoe, A. (2018). AI governance: a research agenda. Governance of AI Program. Future of Humanity Institute, University of Oxford: Oxford, UK; 1442-1443. ‏
  17. Edler, J., Gök, A., Cunningham, P., & Shapira, P. (2016). Introduction: Making sense of innovation policy. In Handbook of Innovation Policy Impact. Edward Elgar Publishing.
  18. European commission. (2003). Annual Innovation Policy Trends and Appraisal Report for Turkey, A publication from the innovation/SMEs Program.
  19. Fartash, K., Mohseni Kiasari, M., & Mesma Khosroshahi, E. (2021). Analyzing Policy Instruments Used in Technological Collaboration of 3 Large Firms with Small Technology-based Firms. Journal of Science & Technology Policy, 14(3), 1-18. {In Persian}.
  20. Flanagan, K., Elvira, U., and Manuel, L. (2011). The ‘policy mix’for innovation: rethinking innovation policy in a multi-level. Research Policy 40.5: 702-713.
  21. Freeman, C. 1987. Technology and economic performance: Lessons from Japan. London: Pinter.
  22. Ghazinoori, S., Ghazinoori, S. (2012). Science and technology policy in the form of general and specific policies. Rahyaft, 22(50), -.{In Persian}.
  23. Ghazinoori, S., Radaei, N. (2019). The Framework for STI Policy Programs. Journal of Science and Technology Policy, 12(2), 527-542. {In Persian}.
  24. Givoni, M., Macmillen, J., Banister, D., & Feitelson, E. (2013). From policy measures to policy packages. Transport Reviews, 33(1), 1-20. ‏
  25. Holdren, J.P.; Bruce, A.; Felten, E.; Lyons, T.; Garris, M. (2016). Preparing for the future of AI. US National Science and Technology Council.
  26. Howlett, M., & Rayner, J. (2013). Patching vs packaging in policy formulation: Assessing policy portfolio design. Politics and Governance. 1(2), 170-182. ‏
  27. Hughes, J.J. (2007). Global technology regulation and potentially apocalyptic technological threats. In Nanoethics. John Wiley: Hoboken, NJ, USA. pp. 201–214.
  28. (2019). Global AI Talent Report 2020. Available at: Link
  29. Joy, B. (2000). Why the future doesn’t need us. Wired 2000, 8, 238–263
  30. Kergroach, S., Meissner, D., & Vonortas, N. S. (2018). Technology transfer and commercialisation by universities and PRIs. Economics of Innovation and New Technology, 27(5-6), 510-530. ‏
  31. Lundvall, B. (1992). National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning. London: Pinter Publishers.
  32. Majaski, C. (2022). Understanding a Developed Economy.Investopedia. Available at: Link
  33. (2021). Iran in the mirror of the artificial. Available at: Link.{In Persian}.
  34. Meissner, D., & Kergroach, S. (2021). Innovation policy mix: mapping and measurement. The Journal of Technology Transfer, 46(1), 197-222. ‏
  35. Mohammadi, D., Salimifard, K., Yousefi, S. (2014). Investigating the performance of the most common multi-criteria decision making. Journal of Operational Research in its Applications. 11(1), 65-84. {In Persian}.
  36. Mohseni Kiasari, M., Mohammadi, M., Jafarnejad, A., Garousi Mokhtarzadeh, N., & Asadifard, R. (2017). Classification of Demand-based Innovation Policy Tools Using Meta-Synthesis Approach. Innovation Management Journal, 6(2), 109-138. {In Persian}.
  37. Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence. Springer, Cham. ‏555-572.
  38. Nasiri, H., Radaei, N. (2019). Classification and Choice of Science, Technology and Innovation Policy Instruments. Journal of Science and Technology Policy, 12(2), 495-511. {In Persian}.
  39. Noroozi Chakoli, A., & Madadi, Z. (2015). Impact of economic power on science and technology situation of countries and the analyzing of their cross-relations. Scientometrics Research Journal, 1(2), 1-14. doi: 10.22070/rsci.2016.379. {In Persian}.
  40. (2019). Going Digital: Shaping Policies, Improving Lives, OECD Publishing, Paris.
  41. (2020). OECD Digital Economy Outlook 2020, OECD Publishing.
  42. Pakzad, M., Ghazinoori, S., & Mohammadi, M. (2020). Designing a Smart Specialization Model for Innovation Development in the Provinces of Iran: Case Study of East Azerbaijan Province. Strategic Management Researches, 26(78), 73-98. {In Persian}.
  43. (2017). Sizing the prize: PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution. Available at: Link
  44. Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process? European Journal of Operational Research, 48(1), 9-26. http://dx.doi.org/10.1016/0377- 2217(90)90057-I
  45. Seyedan, M. (2019). Developing STI Policies for developing countries: Conceptual model and comparative analysis. Journal of Industry and University, 27(), 13-26. {In Persian}.
  46. Siau, K., & Wang, W. (2018). Building trust in AI, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47–53.
  47. Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303-318
  48. (2022). The Global AI Index - Tortoise. Available at: Link
  49. (2022). Human Development Index (HDI). Available at: Link
  50. Zanjirchi, N. (2011). Fuzzy Hierarchy Analysis Process. Sanei Publications. {In Persian}.
  51. Zhang, D et all. (2021). The AI Index 2021 Annual Report,” AI Index Steering Committee, Human-Centered AI Institute, Stanford University.