ABSTRACT This study provides a comprehensive bibliometric analysis of the development of Explainable Artificial Intelligence (XAI) research from 1993 to 2024. The objective is to explore key contributors, thematic trends, and the evolution of methodologies within the field. By employing network analysis, Multiple Correspondence Analysis, and co–citation techniques, the study identifies major research clusters, global collaboration patterns, and the most influential keywords. The results highlight the dual focus of XAI research: the development of technical methods to improve model interpretability and their applications across diverse domains such as healthcare, risk management, and climate science. Furthermore, the historiographic network captures the progression of XAI from foundational concepts to specialized applications, emphasizing its interdisciplinary growth. This analysis offers valuable insights into the trajectory of XAI research, aiming to guide future advancements and promoting further collaboration in this critical area.
Russo et al. (2026) studied this question.