Analyzing Trustworthy in AI: A Comprehensive Bibliometric Review of Artificial Intelligence Research

Abstract

This bibliometric analysis delves into the landscape of Trustworthy Artificial Intelligence (AI) research, revealing intriguing patterns and key insights. Lotka's Law unveils a skewed distribution in author productivity, underscoring the concentration of scholarly output among a select few. Bradford's Law identifies core journals significantly contributing to the field's scientific productivity. Author affiliations shed light on influential institutions, with Tsinghua University and Beijing University of Posts and Telecommunications emerging as major contributors. Examining corresponding authors' countries emphasizes China's dominance in both citations and hosting corresponding authors. Authors with an h-index of 9 and above showcase local impact, with researchers like LI J and ZHANG J standing out. The Collaboration Network visualizes the interconnectedness of researchers, revealing collaborative clusters. Analyzing countries' scientific production underscores China's leadership, with a substantial global contribution. Noteworthy documents and their impact, such as LIU R's work garnering high total citations, illustrate the significance of specific publications. These findings underscore the global and multifaceted nature of Trustworthy AI research, providing valuable insights for future investigations, policy considerations, and international collaborations in this rapidly evolving field.

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