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Publicação
SECURITY AND PRIVACY IN EXPLAINABLE AI: A BIBLIOMETRIC ANALYSIS OF EMERGING LEAKAGE RISKS
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Orientador(es)
Resumo(s)
Explainable Artificial Intelligence (XAI) has gained increasing attention as a means of improving the transparency and trustworthiness of machine learning algorithms, particularly in domains where security and privacy concerns are relevant. This study presents a bibliometric analysis of research at the intersection of explainable artificial intelligence, security, and privacy. The aim was to charac-terize publication trends, thematic structures, and keyword relationships within the field. Scholarly records were retrieved from the Lens database using a structured search strategy based on the PRISMA protocol and analyzed using bibliometric tools, including Bibliometrix and VOSviewer. The total number of studies analyzed was 8,099, and the analyzed time frame was 2010–2025. The analysis examined general publication information, annual scientific production, leading publication venues, and keyword co-occurrence networks. Results indicate a rapid growth in XAI-related publi-cations in recent years and reveal several major thematic clusters, including deep learning–driven medical imaging applications, foundational machine learning and data science concepts, explaina-bility methods in security and distributed learning contexts, and governance-oriented themes related to ethics, privacy, and trust. Overall, the findings highlight the application-driven and interdiscipli-nary nature of explainable AI research, while showing that security and privacy topics, although present, remain relatively peripheral within the broader XAI literature.
Descrição
Palavras-chave
Explainable Artificial Intelligence Bibliometric Analysis Cybersecurity Infor-mation Leakage Machine Learning Data Science Privacy-Preserving ML.
Contexto Educativo
Citação
Matos, M., Lousã, M., & Morais, J., (2026). SECURITY AND PRIVACY IN EXPLAINABLE AI: A BIBLIOMETRIC ANALYSIS OF EMERGING LEAKAGE RISKS. Vol. 32 (1).
Editora
ISPGAYA