Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level

Victor Herrero-Solana; Carmen Gálvez

cs.DLFeb 16, 2026

Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level

Authors: Victor Herrero-Solana, Carmen Gálvez

Organizations: SCImago-UGR, Unit for Computational Humanities and Social Sciences (U^CHASS), University of Granada, Spain · Unit for Computational Humanities and Social Sciences (U^CHASS), University of Granada, Spain

Abstract

This study examines the distribution of Artificial Intelligence (AI) research across European NUTS-3 regions during the period 2015-2024. Using bibliometric data from Clarivate InCites and the Citation Topics classification system, we analyse two hierarchical thematic levels: Electrical Engineering, Electronics & Computer Science (Macro Citation Topic 4) and Artificial Intelligence & Machine Learning (Meso Citation Topic 4.61). Relative Specialization Index (RSI) and Relative Citation Impact (RCI) indicators are calculated for 781 European NUTS-3 regions. While major metropolitan hubs such as Paris, Warszawa, and Madrid dominate in absolute publication volume, the results reveal that the highest levels of relative AI specialization are concentrated in peripheral regions, particularly in Eastern Europe and Spain. Granada and Vilniaus apskritis stand out as regions combining high specialization with strong citation visibility. The analysis further suggests a weak relationship between regional specialization and citation impact, revealing multiple regional profiles, including highly specialized regions with limited citation visibility, highly visible regions with comparatively low specialization, and diversified scientific systems combining moderate specialization with strong citation impact. Fyn emerges as an extreme case of very high citation impact despite relatively low specialization.

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Whose fairness? Structural concentration in AI bias research

Abhash Shrestha, Subigya Gautam, Anu Sapkota, Sanju Tiwari+1

1*Center for Artificial Intelligence (AI) Research Nepal, Sundarharaincha-09, Koshi, Nepal. · 2Sharda University, Delhi-NCR,India. · 3Shodhguru Innovation and Research Labs, India.+1

Algorithmic Fairness Algorithmic Bias

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Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias. Yet the fairness definitions, benchmarks, and debiasing frameworks on which this methodology rests are treated as universal while being produced by a research community whose composition has never been characterized. We show that the AI bias research are structurally concentrated, and that this concentration is greatest, geographically, in precisely the domain the rest of the field inherits from. Analyzing 692 publications spanning five thematic domains, combining bibliometric analysis with semantic clustering, we find that research activity is dominated by a small set of countries, institutions, and authors, with the United States leading publication output and collaboration networks across every domain and most strongly in general fairness and bias mitigation, the largest, most-cited domain with meaningful representation across all four semantic clusters. Low- and middle-income countries remain largely absent from the community and its collaboration networks, and citation influence is highly skewed (median = 9; mean =93.5 ), indicating that a small fraction of publications disproportionately shapes the field. Because the general-fairness domain supplies the definitions and benchmarks that application areas apply, concentration of research effort in this foundational domain propagates across AI bias research as a whole - raising the concern that mitigation methods developed and validated within a narrow set of contexts may not generalize to all populations and settings where AI is deployed. We provide an interactive atlas for continuous monitoring of the field's structure.

Abstract

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