Global artificial intelligence governance research in the digital and intelligent era: advances, trends and countermeasures

Purpose In the era of the intelligent science paradigm, AI governance has emerged as a focal point of global attention. The purpose of this study is to systematically review the current status of global AI governance research, which contributes to advancing academic research in this field and provides a reference for enhancing the effectiveness and level of social governance. Design/methodology/approach Employing an integrated methodological approach, this study systematically investigates the global landscape of AI governance research progress, emerging trends and social focus through triangulated analysis of bibliometric data from Web of Science, CNKI and Dimensions databases, altmetrics indicators from Altmetric.com and Overton platform and qualitative content analysis. This study analyzes a comprehensive corpus of 9,931 AI governance publications, comprising 6,905 English-language articles from Web of Science and 3,026 Chinese publications from CNKI. Meanwhile, Overton platform and Dimensions database are used to investigate the distribution of published articles on AI governance policies and regulations and cited by policies. Additionally, Altmetric.com provided nine altmetrics indicators for 3,120 AI governance publications, yielding a total of 420,919 altmetric mentions. Findings This study identifies four major research themes in current AI governance and establishes their theoretical frameworks while examining key research hotspots. Through altmetrics analysis, it reveals social focus regarding AI governance and analyzes influential articles with significant academic and societal influence, subsequently predicting development trends in this field. Furthermore, the research systematically reviews and summarizes global AI governance policies & regulations, along with associated risks. Guided by the dual governance goals of sustainable development and human-centric approaches for global AI governance, and grounded in multi-stakeholder collaborative governance theory, this work incorporates agile governance, classified governance and anticipatory governance ideologies. Ultimately, it proposes a comprehensive AI governance framework with corresponding governance countermeasures. Originality/value By integrating bibliometric analysis of academic influence with altmetric assessment of societal influence, this study systematically maps AI governance’s research progress, identifies four key thematic clusters and reveals critical gaps between academic focus and public concerns. In addition, this study also systematically sorts out AI governance policies & regulations and critically analyzes the inherent risks associated with AI and proposes an AI governance framework and countermeasure.