Jul 2026· Trends in Social Sciences and Humanities Research· 0 citations· 30 references
TL;DR
A shift toward a people-centered governance paradigm is needed, where human-AI collaboration and inclusive governance establish a dynamic balance between efficiency and equity, innovation and regulation.
Abstract
Artificial intelligence is profoundly reshaping the paradigm of urban governance. On one hand, AI brings efficiency gains, resource optimization, and enhanced resilience — automated administration, precise services, and intelligent operations make cities run more efficiently. On the other hand, the accompanying risks cannot be ignored: algorithm optimization, responsible data governance, transparent decision-making, and inclusive digital development. These challenges highlight the importance of balancing technological innovation with public values. To overcome this predicament, a shift toward a people-centered governance paradigm is needed, where human-AI collaboration and inclusive governance establish a dynamic balance between efficiency and equity, innovation and regulation.
Water reservoir governance in Chhattisgarh faces a distinct structural paradox: an abundance of natural hydrometeorological resources coexisting with severe localized seasonal deficits, low creation-to-utilization ratios of irrigation
potential, and complex inter-sectoral competing demands from agriculture, energy, and...
Bharati Sahu, N. Jahan, Deepika Rajwade et al.· International Journal of Inn...· 0 citations
The function of AI in the field of sustainable governance is discussed and global publication trends, thematic interests, and collaborative patterns from 2000 to 2019 are examined and underscores the importance of concerted interdisciplinary research efforts to strengthen the resilience, trust, and transparency of gove...
A systematic literature review and bibliometric analysis of 64 scientific articles focused on the frameworks underpinning adaptive smart urban systems reveal a strong convergence between AI, the Internet of Things, and Big Data, as well as significant limitations in terms of interoperability, data governance, and scala...
Gary Reyes, Roberto Tolozano-Benites, Jorge Reyes et al.· Information· 0 citations
The findings suggest that local governments should tailor AI research, development, and application strategies according to their cities’ resource endowments and developmental foundations and enriches empirical evidence on AI’s role in reducing LRM.
Long Xin, Yidan Liu, Yuyao Wang et al.· Land· 0 citations
To manage complex disaster risks, it is important to develop a response that spans multiple areas of expertise. Bridging the gap between law, social sciences, and natural sciences is an important part of any disaster risk reduction. It helps systems adapt quickly as AI technologies are constantly changing and have sign...
R. Paper, Research Supervisor Prof, Dominique Ferraro· The social science· 0 citations
Rapid urbanization and escalating climate-related risks have significantly heightened the disaster vulnerability of cities worldwide. Traditional urban planning methods, constrained by their reliance on static historical data and reactive strategies, are increasingly inadequate for addressing contemporary hazard comple...
Senthil M· 2026 11th International Conf...· 0 citations
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