Measuring AI Capacity in Countries with National AI Strategies: An Integrated D-CRITIC & SPOTIS Approach

Abstract

Artificial Intelligence (AI) is a strategic element that supports countries' innovation capacities and their ability to achieve sustainable development goals on a global scale. With the development of AI, monitoring global policies has become crucial. In this regard, countries should be aware of the opportunities and threats associated with AI, change their existing policies, monitor their AI capacities, and take measures to improve their performance. There are indices based on subjective assessments for tracking AI capacity performance. However, in order to ensure the reliability and objectivity of the rankings, the assessment must be carried out using objective weighting methods. Motivated by this, the study aims to assess the AI capacities of upper-middle-income countries with national AI strategies objectively and comparatively. In this regard, the importance levels of the factors determining AI capacity were determined using Global AI Index data with the D-CRITIC method, and countries' performances were ranked using the SPOTIS approach. Furthermore, the sensitivity of the rankings was tested with sensitivity analyses conducted under different weighting scenarios, and the robustness of the results obtained was examined. The consistency of the rankings obtained with D-CRITIC and SPOTIS was also measured through comparative analysis. The results showed that the most critical dimension in determining AI capacity is the operating environment. In terms of performance between countries, China, Turkey, and Argentina were found to be the countries with the highest AI capacity in the upper-middle income group. The sensitivity analysis showed that the AI capacity rankings of countries at the top of the list were generally stable. In contrast, it was found that the performance of some countries in the middle and lower ranks was sensitive to criterion weights. The comparative analysis showed that the results of the study are consistent and compatible with other methods, and the rankings are not sensitive to methods. The findings confirm the robustness of countries' performance but also reveal that certain countries have a strategically more fragile structure.

Keywords

• Artificial Intelligence
• Performance measurement
• digitalization
• sustainability
• d-critic
• spotis

Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı

Abstract

Yapay zekâ küresel boyutta ülkelerin yenilikçilik kapasitelerini, sürdürülebilir kalkınma hedeflerine ulaşma yetkinliklerini destekleyen stratejik bir unsurdur. Yapay zekânın gelişmesiyle birlikte küresel politikaların izlenmesi daha önemli hale gelmektedir. Bu doğrultuda, ülkeler yapay zekâ ile ilişkili fırsatları ve tehditleri farkında olarak mevcut politikalarını değiştirmeli, yapay zekâ kapasitelerini izlemeli ve performanslarını iyileştirici önlemler almalıdır. Yapay zekâ kapasite performanslarının takip edilebilmesi için subjektif değerlendirmelere dayanan endeksler bulunmaktadır. Ancak sıralamaların güvenilirliği ve nesnelliğini sağlayabilmek adına objektif ağırlıklandırma yöntemleri kullanılarak değerlendirmenin yapılması gerekmektedir. Bu motivasyonla çalışmada üst-orta gelir grubunda yer alan ve ulusal yapay zekâ stratejisine sahip ülkelerin yapay zekâ kapasitelerini nesnel ve karşılaştırmalı bir biçimde değerlendirilmesi amaçlanmıştır. Bu doğrultuda, Global AI Index verileri kullanılarak yapay zekâ kapasitesini belirleyen faktörlerin önem dereceleri D-CRITIC yöntemiyle ortaya konmuş, ülkelerin performansları da SPOTIS yaklaşımıyla sıralanmıştır. Ayrıca farklı ağırlıklandırma senaryoları altında yapılan duyarlılık analizleriyle sıralamaların duyarlılığı test edilmiş, elde edilen sonuçların sağlamlığı incelenmiştir. D-CRITIC ve SPOTIS ile elde edilen sıralamaların diğer yöntemlerle tutarlılığı da karşılaştırmalı analiz ile ölçülmüştür. Sonuçlar, yapay zekâ kapasitesini belirlemede en kritik boyutun işletim ortamı olduğunu göstermiştir. Ülkeler arası performanslarda ise Çin, Türkiye ve Arjantin’in üst-orta gelir grubunda en yüksek yapay zekâ kapasitesine sahip ülkeler olduğu ortaya koyulmuştur. Duyarlılık analizi sonucunda ise özellikle üst sıralarda yer alan ülkelerin yapay zekâ kapasite sıralamalarının genel olarak istikrarlı olduğu görülmüştür. Buna karşılık orta ve alt sıralarda yer alan bazı ülkelerin performanslarının kriter ağırlıklarına duyarlı olduğu tespit edilmiştir. Karşılaştırmalı analiz sonucunda çalışmanın sonuçlarının tutarlı ve diğer yöntemlerle uyumlu olduğu, sıralamaların yöntem seçimine duyarlı olmadığı gösterilmiştir. Elde edilen bulgular, ülkelerin performanslarının sağlamlığını teyit etmekle birlikte, belirli ülkelerin stratejik olarak daha kırılgan bir yapıya sahip olduklarını da ortaya koymuştur.

Keywords

• Yapay zeka
• performans ölçümü
• dijitalleşme
• sürdürülebilirlik
• d-critic
• spotis

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