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Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes

Author

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  • Manta Eduard Mihai
  • Bogoevici Flavia

    (Doctoral School of Cybernetics and Statistics, The Bucharest University of Economic Studies, Romania)

Abstract

This study develops a scorecard validation model for evaluating key Artificial Intelligence (AI) indexes, aiming to provide a comprehensive framework for assessing the multifaceted nature of AI development. Focusing on four significant AI indexes and one AI report from 2021 to 2023, the research employs both expert judgment and advanced text mining techniques, including k-means clustering. This dual approach facilitates a detailed examination of AI indexes, highlighting their strengths, weaknesses, and overall market comprehensiveness. The findings contribute to understanding the AI sector’s evolution, offering critical insights for policy formulation and strategic decision-making in AI. Acknowledging the inherent subjectivity in the evaluation process and potential data biases, the paper suggests future research avenues, including cross-sectoral and regional analyses of AI trends and a deeper exploration of ethical considerations in AI. This study serves as a valuable resource for stakeholders navigating the complex AI landscape, providing a structured method for comparing and understanding AI advancements.

Suggested Citation

  • Manta Eduard Mihai & Bogoevici Flavia, 2023. "Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes," Journal of Social and Economic Statistics, Sciendo, vol. 12(2), pages 88-108, December.
    • Handle: RePEc:vrs:jsesro:v:12:y:2023:i:2:p:88-108:n:6
    DOI: 10.2478/jses-2023-0011
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    References listed on IDEAS

    as
    1. Artem Bequé & Kristof Coussement & Ross Gayler & Stefan Lessmann, 2017. "Approaches for credit scorecard calibration: An empirical analysis," Post-Print hal-01745262, HAL.
    2. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    AI; text analysis; scorecard validation; AI indexes; content analysis;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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