Artificial Intelligence in Social Governance: Global Insights from Theoretical Frameworks to Practical Applications Using CiteSpace
Abstract
This study presents a comprehensive examination of the evolving role of artificial intelligence (AI) in social governance through an integrated bibliometric, theoretical, and case-based methodology. Utilizing CiteSpace-based keyword co-occurrence and burst analyses of data from 2014 to 2024, it identifies ten thematic clusters that illustrate a dual developmental trajectory: a vertical deepening of governance theories and technological paradigms, and a horizontal expansion into domains such as smart cities, environmental governance, and algorithmic administration. A critical review of international theoretical frameworks—including the Technology Acceptance Model, Diffusion of Innovation Theory, Socio-Technical Systems Theory, Algorithmic Governance Theory, and Digital Governance Theory—reveals both their analytical value and limitations in the context of Chinese governance. Comparative case studies, encompassing domestic initiatives such as “City Brain,” smart communities, smart courts, and digital villages, alongside international examples from Singapore, Estonia, and the European Union, highlight China's rapid policy-driven advancements as well as enduring challenges in algorithmic transparency, cross-departmental data integration, and public participation. The study identifies four key governance challenges: data security and privacy, algorithmic ethics and transparency, the digital divide, and coordination inefficiencies. Looking forward, it outlines future trajectories shaped by multimodal AI, generative AI, and digital twin technologies. Policy recommendations call for standardized data governance protocols, robust AI ethics frameworks, targeted digital literacy programs, and enhanced multi-stakeholder collaboration. The findings advance a practice-oriented, interdisciplinary research agenda for intelligent social governance and provide actionable insights for aligning technological innovation with institutional transformation in the AI era.
Keywords:
Artificial intelligence; Social governance; Bibliometric analysis; Algorithmic governance; Smart cities; Digital governmentCopyright Notice & License:
All articles published in Global Review of Humanities, Arts, and Society (GRHAS) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows anyone to share, copy, distribute, transmit, and adapt the work, including for commercial purposes, provided that proper attribution is given to the original author(s) and the source.
This work is licensed under a Creative Commons Attribution 4.0 International License.
PDF Downloads
Journal Info
How to Cite
References
Barrett, A. M., Newman, J., Nonnecke, B., Madkour, N., Hendrycks, D., Murphy, E. R., Jackson, K., & Raman, D. (2025). AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models. arXiv preprint arXiv:2506.23949.
Bostrom, N., & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57-69). Chapman and Hall/CRC.
Brynjolfsson, E. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies (Vol. 236). WW Norton Company.
Daly, T. G. (2019). Democratic decay: Conceptualising an emerging research field. Hague Journal on the Rule of Law, 11(1), 9-36.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Hoe, S. L. (2025). From a learning to a smart nation: the rise of the digitalization megatrend and Singapore’s development. Journal of Electronic Business & Digital Economics, 4(1), 73-87.
Kirk, C., Küderle, A., Micó-Amigo, M. E., Bonci, T., Paraschiv-Ionescu, A., Ullrich, M., Soltani, A., Gazit, E., Salis, F., & Alcock, L. (2024). Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Scientific reports, 14(1), 1754.
Kumar, D. (2024). AI-driven automation in administrative processes: Enhancing efficiency and accuracy. International Journal of Engineering Science and Humanities, 14(Special Issue 1), 256-265.
Li, Y., Wen, X., & Gao, W. (2022). Exploration on timing adaptation of coordinated development of digital villages and rural revitalization. Agricultural Economics and Management, 4, 1-12.
Margetts, H., & Dunleavy, P. (2013). The second wave of digital-era governance: a quasi-paradigm for government on the Web. Philosophical transactions of the royal society A: mathematical, physical and engineering sciences, 371(1987), 20120382.
Mpekoa, N., & Van Greunen, D. (2017). E-voting experiences: A case of Namibia and Estonia. 2017 IST-Africa Week Conference (IST-Africa),
Papagianneas, S. (2022). Towards smarter and fairer justice? A review of the Chinese scholarship on building smart courts and automating justice. Journal of Current Chinese Affairs, 51(2), 327-347.
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885.
Pereira, F., Lee, B., & Fife, E. (2023). Singapore's Smart Nation Program and potential lessons for the European Union's Digital Decade program.
Qin, H., Shao, S., Wang, T., Yu, X., Jiang, Y., & Cao, Z. (2023). Review of autonomous path planning algorithms for mobile robots. Drones, 7(3), 211.
Reuel, A., & Undheim, T. A. (2024). Generative AI needs adaptive governance. arXiv preprint arXiv:2406.04554.
Rogers, E. (2003). Diffusion of Innovations 5th. In: Free press.
Roh, Y., Heo, G., & Whang, S. E. (2019). A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1328-1347.
Sahin, I., & Thompson, A. (2006). Using Rogers’ theory to interpret instructional computer use by COE faculty. Journal of Research on Technology in Education, 39(1), 81-104.
Tallberg, J., Erman, E., Furendal, M., Geith, J., Klamberg, M., & Lundgren, M. (2023). The global governance of artificial intelligence: Next steps for empirical and normative research. International Studies Review, 25(3), viad040.
Veale, M., Matus, K., & Gorwa, R. (2023). AI and global governance: Modalities, rationales, tensions. Annual Review of Law and Social Science, 19(1), 255-275.
Verma, S. (2019). Weapons of math destruction: how big data increases inequality and threatens democracy. Vikalpa, 44(2), 97-98.
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & governance, 12(4), 505-523.
Zuboff, S. (2023). The age of surveillance capitalism. In Social theory re-wired (pp. 203-213). Routledge.
