Factors Influencing AI Index Across Regions, Political Regimes, and Development Indicators: A Random Forest and K-Mean Clustering Approach

This paper aims to model the determinants of the Global AI Index by selecting one of the eight regions or political regimes, as well as one level of development at a time and performs Random Forest and K-Means clustering analysis on these indicators. The study selects 62 countries that are most prepared for the application of AI technology and addresses their specific precursors, which include research, development, talent, and infrastructure. The Random Forest model points to the contribution of development (29.3 %) and research (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 5. 3 \%}$</tex>) and clustering analysis categorizes the nations into different clusters depending on AI performance. The findings highlighted the complexities of AI development based on socio-economic and political factors, and provided practical directions for promoting AI development around the world.

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