Ethics, transparency, and explainability in generative ai decision-making systems: a comprehensive bibliometric study

Abstract

ABSTRACT This study conducts a bibliometric analysis of the evolution of ethics, transparency, and explainability in generative Artificial Intelligence (AI) within decision − making systems from 2004 to 2024. Utilising VOSviewer and Biblioshiny tools, literature sourced from Scopus and Web of Science was analyzed following PRISMA guidelines. The findings highlight the rapid expansion of generative AI technologies, particularly since 2019, with a growing focus on ethical frameworks, especially in healthcare. The analysis underlines that as AI systems become more embedded in high-stakes decisions, aligning these systems with societal values is increasingly urgent. The United States and Europe lead in contributors, with significant insights from Asia. Key themes include AI’s ethical challenges, algorithmic transparency, and explainability, with these gaining prominence during the COVID − 19 pandemic. By identifying trends such as the surge in chatbot and large language model research, this research provides a foundation for future studies on ethical AI and informs policy considerations for responsible AI innovation.