<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>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.</p>
</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.

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

Doctoral School of Cybernetics and Statistics

The Bucharest University of Economic Studies

Journal of Social and Economic Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes"}

This study develops a scorecard validation model for evaluating key Artificial Intelligence (AI) indexes, aiming to provide a comprehensive framework for...

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

Publié en ligne: 02 févr. 2024

Pages: 88 - 108

DOI: https://doi.org/10.2478/jses-2023-0011

Mots clés
AI, text analysis, scorecard validation, AI indexes, content analysis

© 2023 Eduard Mihai Manta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Publié en ligne: 02 févr. 2024

Pages: 88 - 108

DOI: https://doi.org/10.2478/jses-2023-0011

Mots clésAI, text analysis, scorecard validation, AI indexes, content analysis

© 2023 Eduard Mihai Manta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
AI, text analysis, scorecard validation, AI indexes, content analysis