AI Governance: A systematic review using topic modelling techniques

Abstract

Abstract Abstract This study provides a systematic review of artificial intelligence (AI) governance, utilizing topic modeling techniques to identify and analyze prevailing themes and research trends. The dataset comprises 1161 records from the Web of Science, spanning from 2006 to 2024, and was analyzed using BERTopic to uncover seven major clusters of topics. These clusters cover key areas, including smart cities and sustainable development, digital governance and legal challenges, and technology ethics and decision-making transparency. The findings reveal that AI governance is an interdisciplinary field, involving complex interactions between technical, legal, and ethical dimensions. The study highlights the increasing need for robust frameworks that address transparency, accountability, and the alignment of AI systems with human values. Furthermore, it underscores the importance of international cooperation in developing adaptive and inclusive regulatory measures to mitigate the risks associated with AI technologies. This review contributes to the ongoing discourse by offering a comprehensive overview of existing literature, identifying gaps, and proposing directions for future research, such as the empirical examination of governance models and the exploration of cross-cultural approaches to AI regulation.