ULUSAL YAPAY ZEKA KAPASİTESİNİN ÖLÇÜMÜ: BEŞERİ SERMAYE UZMANLAŞMASINA DAYALI BÜTÜNSEL BİR YAKLAŞIM

Öz

Makalenin amacını, Yapay zekanın dönüştürücü etkisi, teknolojinin benimsenmesi ve adaptasyonu için gerekli becerilerin kazanılması ihtiyacını daha da kritik hale getirmiştir. Bu durum, yapay zeka alanındaki ilerlemelerin ölçülmesi için kriterler, ölçütler ve metodolojilerin geliştirilmesi ihtiyacını ortaya çıkarmıştır. Bu araştırmanın temel amacı, yapay zeka alanında ulusal uzmanlaşma düzeyini tespit etmektir. Bu hedefe ulaşmak için, bilgi birikimi, beceriler, sektörel deneyimler gibi beşeri sermaye unsurları kapsamlı bir şekilde incelenmiştir. Araştırma, üç aşamada gerçekleştirilmiştir. İlk aşamada, beşeri sermayenin uzmanlaşma düzeyini ve ilgili faktörleri inceleyen kapsamlı bir kavramsal çerçeve oluşturulmuştur. Bu çerçeve, çalışmanın teorik temelini oluşturmaktadır. İkinci aşamada, beşeri sermayenin yapay zeka alanındaki uzmanlaşma düzeyini ölçmek için özgün bir model geliştirilmiştir. Bu modelin geliştirilmesinde hibrit bir yaklaşım olan AHP-Gauss yöntemi kullanılmıştır. Araştırmanın son aşamasında, geliştirilen model kullanılarak çalışma kapsamına dahil edilen ülkelerdeki beşeri sermayenin yapay zeka alanındaki uzmanlaşma düzeyi analiz edilmiş ve karşılaştırmalı olarak değerlendirilmiştir. Bu aşama, ulusal düzeydeki yapay zeka yetkinliklerinin ampirik olarak ölçülmesini ve ülkeler arası karşılaştırmalı bir değerlendirme oluşturulmasını sağlamıştır. Araştırmanın bulguları, Amerika Birleşik Devletleri, Birleşik Krallık, Hindistan, Almanya ve Kanada'nın yapay zeka alanında beşeri sermaye uzmanlaşma düzeyi bakımından lider konumda olduğunu ortaya koymaktadır. Çalışmanın sonuçları, yapay zeka alanında beşeri sermayenin geliştirilmesine yönelik stratejik yaklaşımın netleştirilmesi gerekliliğini vurgulamakta ve bu bağlamda yoğunlaştırılması gereken alanlara ışık tutmaktadır. Bu bulgular, ulusal yapay zeka politikalarının formülasyonunda ve beşeri sermaye geliştirme stratejilerinin tasarlanmasında kritik öneme sahip veriler sunmaktadır.

Anahtar Kelimeler

• Yapay zeka yönetimi
• Teknolojik değişimin yönetimi
• Beşeri sermaye
• Uzmanlaşma
• AHP-Gauss

MEASURING NATIONAL ARTIFICIAL INTELLIGENCE CAPACITY: A HOLISTIC APPROACH BASED ON HUMAN CAPITAL SPECIALIZATION

Öz

The transformative impact of artificial intelligence has made the need to acquire necessary skills for technology adoption and adaptation even more critical. This situation has highlighted the need for developing criteria, metrics, and methodologies to measure advancements in the field of artificial intelligence. The primary objective of this research is to determine the national level of specialization in the field of artificial intelligence. To achieve this goal, human capital elements such as knowledge base, skills, and sectoral experiences have been comprehensively examined. The research was conducted in three stages. In the first stage, a comprehensive conceptual framework was developed to examine the specialization level of human capital and related factors. This framework forms the theoretical foundation of the study. In the second stage, an original model was developed to measure the specialization level of human capital in the field of artificial intelligence. The AHP-Gauss method, a hybrid approach, was used in developing this model. In the final stage of the research, the developed model was used to analyze and comparatively evaluate the specialization level of human capital in AI across the countries included in the study scope. This stage enabled the empirical measurement of national artificial intelligence competencies and the establishment of a cross-country comparative assessment. The research findings reveal that the United States, the United Kingdom, India, Germany, and Canada are in leading positions in terms of human capital specialization levels in the field of artificial intelligence. The study's results emphasize the necessity of clarifying the strategic approach for developing human capital in the artificial intelligence domain and shed light on areas that require intensified focus in this context. These findings provide critically important data for the formulation of national artificial intelligence policies and the design of human capital development strategies.

Anahtar Kelimeler

• Artificial intelligence management
• Management of technological change
• human capital
• specialization
• AHP-Gaussian

Kaynakça

1. Aiken, C., Dunham, J., & Zwetsloot, R. (2020). Career preferences of AI talent. Center for Security and Emerging Technology. https://doi.org/10.51593/20200012
2. Albrecht, M., & Aliaga, S. (2023). The transformative power of generative AI. J.P. Morgan Asset Management. https://am.jpmorgan.com/content/dam/jpm-am- aem/global/en/insights/The%20transformative%20power%20of%20generative%20AI. pdf
3. Ammirato, S., Felicetti, A. M., Della Gala, M., Aramo-Immonen, H., Jussila, J. J., & Kärkkäinen, H. (2019). The use of social media for knowledge acquisition and dissemination in B2B companies: An empirical study of Finnish technology industries. Knowledge Management Research & Practice, 17(1), 52–69. https://doi.org/10.1080/14778238.2018.1541779
4. Aslan, F. (2023). Teknoloji geliştirme sürecinin değerlendirilmesi için olgunluk modeli önerisi: ARGE–500 firmalarının faaliyet ve yaklaşımlarına yönelik bir uygulama [Unpublished doctoral dissertation]. Fırat Üniversitesi
5. Aslan, F. (2024). A monitoring framework for progress in artificial intelligence technology: a research based on scientific and technological ındicators. İstanbul İktisat Dergisi- Istanbul Journal of Economics 74(2), 427-459. https://doi.org/10.26650/ISTJECON2024-1393965
6. Aslan, F., & Uzun, H. (2021). Analysis of the vision statement of the fastest growing technology companies in Turkey. Avrasya Uluslararası Araştırmalar Dergisi, 9(29), 367–393. https://doi.org/10.33692/avrasyad.1035729
7. Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, Article 103745. https://doi.org/10.1016/j.jfineco.2023.10374
8. Baguma, R., Mkoba, E., Nahabwe, M., Mubangizi, M. G., Amutorine, M., & Wanyama, D. (2023). Towards an artificial intelligence readiness index for Africa. In P. Ndayizigamiye, H. Twinomurinzi, B. Kalema, K. Bwalya, & M. Bembe (Eds.), Digital- for-development: Enabling transformation, inclusion and sustainability through ICTs (Vol. 1774, pp. 285–303). Springer Nature Switzerland. https://doi.org/10.1007/978-3- 031-28472-4_18

9. Beck, S., Mahdad, M., Beukel, K., & Poetz, M. (2019). The value of scientific knowledge dissemination for scientists—A value capture perspective. Publications, 7(3), 54. https://doi.org/10.3390/publications7030054
10. Bello, M., Caperna, G., Damioli, G., Steffen, M., & Smallenbroek, O. (2022). Tracking country innovation performance: The Innovation Output Indicator 2022. Publications Office of the European Union.
11. Bozeman, B., & Youtie, J. L. (2017). The strength in numbers: The new science of team science. Princeton University Press. Brandt, J., Kreps, S., Meserole, H., Singh, P., & Sisson, M. W. Succeeding in the AI competition with China: A strategy for action. Brookings Institution.
12. Chai, S., Das, S., & Rao, H. R. (2011). Factors affecting bloggers' knowledge sharing: An investigation across gender. Journal of Management Information Systems, 28(3), 309– 342. https://doi.org/10.2753/MIS0742-1222280309
13. Da Silva, L. P. C., Gomes, C. F. S., & Dos Santos, M. (2021, November). Hospitalares a partir do método multicritério AHP-Gaussiano. XXVIII Simpósio de Engenharia de Produção (SIMPEP 2021), Bauru, Brasil.
14. De Grip, A. (2006). Evaluating human capital obsolescence (ROA Working Paper No. 2E). Maastricht University, Research Centre for Education and the Labour Market (ROA).
15. Diaz-Infante, N., Lazar, M., Ram, S., & Ray, A. (2022). Demand for online education is growing: Are providers ready? McKinsey & Company. https://www.mckinsey.com/industries/education/our-insights/demand-for-online- education-is-growing-are-providers-ready#
16. Dos Santos, V. R., Fávero, L. P. L., Lellis Moreira, M. Â., Dos Santos, M., De Oliveira, L. D. A., Costa, I. P. D. A., Capela, G. P. D. O., & Kojima, E. H. (2023). Development of a computational tool in the Python language for the application of the AHP-Gaussian method. Procedia Computer Science, 221, 354–361. https://doi.org/10.1016/j.procs.2023.07.048
17. Ducheneaut, N. (2005). Socialization in an open source software community: A socio-technical analysis. Computer Supported Cooperative Work (CSCW), 14(4), 323–368. https://doi.org/10.1007/s10606-005-9000-1
18. Engler, A. (2021). How open-source software shapes AI policy. Brookings Institution. https://www.brookings.edu/research/how-open
19. European Commission. (2021). Digital Economy and Society Index (DESI) report. European Commission. https://digital-strategy.ec.europa.eu/en/library/digital-economy-and- society-index-desi-2021
20. European Investment Bank. (2014). Marketing, communication and knowledge dissemination strategies for JESSICA operations. European Investment Bank. https://www.eib.org/attachments/documents/jessica_a21_mck_final_report_en.pdf
21. Fatima, S., Desouza, K. C., & Dawson, G. S. (2020). National strategic artificial intelligence plans: A multi-dimensional analysis. Economic Analysis and Policy, 67, 178–194. https://doi.org/10.1016/j.eap.2020.07.008
22. Foray, D., Goddard, J., Beldarrain, X. G., Landabaso, M., McCann, P., Morgan, K., Nauwelaers, C., & Ortega-Argilés, R. (2012). Guide to research and innovation strategies for smart specialisations. European Commission.
23. Frigenti, L., & Stiles, T. A. A. (2019). 2019 Change Readiness Index. KPMG. https://home.kpmg/xx/en/home/insights/2019/01/change-readiness-index.html
24. Grievson, O., Holloway, T., & Johnson, B. (Eds.). (2022). A strategic digital transformation for the water industry. IWA Publishing. https://doi.org/10.2166/9781789063400
25. Hardeman, S., Van Roy, V., Vertesy, D., & Saisana, M. (2013). An analysis of national research systems (I): A composite indicator for scientific and technological research excellence (EUR No. 26093; JRC No. 83723). Publications Office of the European Union.
26. Hauge, Ø., Ayala, C., & Conradi, R. (2010). Adoption of open source software in software- intensive organizations – A systematic literature review. Information and Software Technology, 52(11), 1133–1154. https://doi.org/10.1016/j.infsof.2010.05.008
27. International Labour Organization. (2012). International Standard Classification of Occupations: ISCO-08. International Labour Office.
28. Keese, M., & Tan, J.-P. (2013). Indicators of skills for employment and productivity: A conceptual framework and approach for low-income countries. Organisation for Economic Co-operation and Development. http://www.oecd.org/g20/topics/development/indicators-of-skills-employment-and- productivity.pdf
29. Korkmaz, G., Santiago Calderón, J. B., Kramer, B. L., Guci, L., & Robbins, C. A. (2024). From GitHub to GDP: A framework for measuring open source software innovation. Research Policy, 53(3), 104954. https://doi.org/10.1016/j.respol.2024.104954
30. Lanvin, B., & Monteiro, F. (2023). The Global Talent Competitiveness Index 2023. INSEAD Business School, Adecco Group, and Human Capital Leadership Institute.
31. LaPrade, A., Mertens, J., Moore, T., & Wright, A. (2020). The enterprise guide to closing the skills gap. IBM Institute for Business Value. https://www.ibm.com/thinking- leadership/institute-business-value/report/closing-skills-gap
32. Liu, H., Yang, G., Liu, X., & Song, Y. (2020). R&D performance assessment of industrial enterprises in China: A two-stage DEA approach. Socio-Economic Planning Sciences, 71, 100753. https://doi.org/10.1016/j.seps.2019.100753
33. Montoya, L., & Rivas, P. (2019). Government AI readiness meta-analysis for Latin America and the Caribbean. 2019 IEEE International Symposium on Technology and Society (ISTAS), 1–8. https://doi.org/10.1109/ISTAS48451.2019.8937869
34. Mou, X. (2019). Artificial intelligence: Investment trends and selected industry uses. International Finance Corporation. https://doi.org/10.1596/32652 National Science and Technology Council. (2016). Preparing for the future of artificial intelligence. Executive Office of the President. https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/o stp/NSTC/preparing_for_the_future_of_ai.pdf
35. National Science and Technology Council. (2023). National artificial intelligence research and development strategic plan. Executive Office of the President. https://www.whitehouse.gov/wp-content/uploads/2023/05/National-Artificial- Intelligence-Research-and-Development-Strategic-Plan-2023-Update.pdf
36. National Science Board. (2020). Vision 2030 (NSB No. 2020-15). National Science Board. https://www.nsf.gov/nsb/publications/2020/nsb202015.pdf National Science Board. (2022). The state of U.S. science and engineering 2022 (NSB No. 2022-1). National Science Board.
37. Organisation for Economic Co-operation and Development. (2011a). Regions and innovation policy. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/9789264082052-en
38. Organisation for Economic Co-operation and Development. (2011b). Skills for innovation and research. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/9789264097490-en
39. Organisation for Economic Co-operation and Development. (2013). Supporting investment in knowledge capital, growth and innovation. OECD Publishing. https://doi.org/10.1787/9789264193307-en
40. Organisation for Economic Co-operation and Development. (2015). World indicators of skills for employment (WISE). Organisation for Economic Co-operation and Development. https://www.oecd.org/employment/skills-for-employment-indicators.htm
41. Organisation for Economic Co-operation and Development, AI Policy Observatory. (2024). Live data. OECD.AI Policy Observatory. https://oecd.ai/en/data?selectedArea=ai-jobs- and-skills
42. Orkestra, J. W. (2020, September). Supporting skills for industry through clusters. European Union. https://www.clustercollaboration.eu
43. Parameswaran, M., & Whinston, A. (2007). Research issues in social computing. Journal of the Association for Information Systems, 8(6), 336–350. https://doi.org/10.17705/1jais.00132
44. Pereira, R. C. A., Da Silva, O. S., De Mello Bandeira, R. A., Dos Santos, M., De Souza Rocha, C., Castillo, C. D. S., Gomes, C. F. S., De Moura Pereira, D. A., & Muradas, F. M. (2023). Evaluation of smart sensors for subway electric motor escalators through AHP- Gaussian method. Sensors, 23(8), 4131. https://doi.org/10.3390/s23084131
45. Rao, A. S., & Verweij, G. (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PwC. https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize- report.pdf
46. Rodrigues, M., Fernández-Macías, E., & Sostero, M. (2021). A unified conceptual framework of tasks, skills and competences (JRC No. 121897). European Commission.
47. Tuccio, M. (2019). Measuring and assessing talent attractiveness in OECD countries (OECD Social, Employment and Migration Working Paper No. 229). Organisation for Economic Co-operation and Development. https://doi.org/10.1787/b4e677ca-en
48. UNCTAD (Ed.). (2019). Building digital competencies to benefit from frontier technologies. United Nations.
49. Vandeweyer, M., Reznikova, L., Espinoza, R., Lee, M., & Herabat, T. (2020). Thailand’ s education system and skills imbalances: Assessment and policy recommendations (OECD Economics Department Working Paper No. 1641). Organisation for Economic Co-operation and Development. https://doi.org/10.1787/b79addb6-en
50. World Economic Forum. (2022). Empowering AI leadership: AI C-suite toolkit. World Economic Forum. https://www.weforum.org/publications/empowering-ai-leadership- ai-c-suite- toolkit/?gad_source=1&gad_campaignid=22234048793&gbraid=0AAAAAoVy5F5a_ ae5KrrmPudqxkF- 1bJSO&gclid=CjwKCAiAuIDJBhBoEiwAxhgyFowM6yOdgrccRjfOXd8roO- TMHvZdq6lcUPph9OSsuDOh7KSYajB3hoCbK8QAvD_BwE