Research Trends and Networks in Self-Explaining Autonomous Systems: A Bibliometric Study

Self-Explaining Autonomous Systems (SEAS) have emerged as a strategic frontier within Artificial Intelligence (AI), responding to growing demands for transparency and interpretability in autonomous decision-making. This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025, drawing upon 1380 documents indexed in Scopus. The analysis applies co-citation mapping, keyword co-occurrence, and author collaboration networks using VOSviewer, MASHA, and Python to examine scientific production, intellectual structure, and global collaboration patterns. The results indicate a sustained annual growth rate of 41.38%, with an h-index of 57 and an average of 21.97 citations per document. A normalized citation rate was computed to address temporal bias, enabling balanced evaluation across publication cohorts. Thematic analysis reveals four consolidated research fronts: interpretability in machine learning, explainability in deep neural networks, transparency in generative models, and optimization strategies in autonomous control. Author co-citation analysis identifies four distinct research communities, and keyword evolution shows growing interdisciplinary links with medicine, cybersecurity, and industrial automation. The United States leads in scientific output and citation impact at the geographical level, while countries like India and China show high productivity with varied influence. However, international collaboration remains limited at 7.39%, reflecting a fragmented research landscape. As discussed in this study, SEAS research is expanding rapidly yet remains epistemologically dispersed, with uneven integration of ethical and human-centered perspectives. This work offers a structured and data-driven perspective on SEAS development, highlights key contributors and thematic trends, and outlines critical directions for advancing responsible and transparent autonomous systems.