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European Sovereignty in Artificial Intelligence: A Competence-Based Perspective

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  • Ludovic Dibiaggio

    (SKEMA Business School - SKEMA Business School, UniCA - Université Côte d'Azur, GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Lionel Nesta

    (UniCA - Université Côte d'Azur, GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur, OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)

  • Simone Vannuccini

    (UniCA - Université Côte d'Azur, GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

Abstract

We present a first-of-its-kind empirical study of technological sovereignty in artificial intelligence, adopting a competence-based perspective. We use patents and publication data to map competencies across AI techniques, functions and applications, and develop a novel measure of integration based on relative specializations and complementarities. We argue that our measure approximates technological sovereignty by capturing local capabilities to innovate in AI. We use our novel measure to explain AI innovation, and unpack integration determinants. Our focus is on the European Union, given its lagging position yet key role in a global landscape increasingly characterized by growing rivalries and fragmentation.
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Suggested Citation

  • Ludovic Dibiaggio & Lionel Nesta & Simone Vannuccini, 2024. "European Sovereignty in Artificial Intelligence: A Competence-Based Perspective," Working Papers hal-04841182, HAL.
    • Handle: RePEc:hal:wpaper:hal-04841182
    Note: View the original document on HAL open archive server: https://hal.science/hal-04841182v1
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