Artificial Intelligence in Crisis Management and Public Policy
Keywords:
Artificial intelligence, crisis management, public policy, data-driven governance, institutional resilienceAbstract
Objective: This study aims to systematically review existing literature to identify the roles, applications, and challenges of artificial intelligence (AI) in crisis management and public policy.
Methods and Materials: This qualitative review employed a thematic content analysis approach. The research population consisted of scholarly articles published between 2015 and 2024 in major academic databases including Scopus, Web of Science, and ScienceDirect. Based on inclusion criteria—direct relevance to the topic, a clear theoretical framework, and peer-reviewed publication—twelve studies were selected. Data analysis was conducted using Nvivo version 14, following open, axial, and selective coding until theoretical saturation was achieved.
Findings: The analysis revealed three main themes: (1) the multifaceted role of AI in crisis management, including prediction, early warning, decision-making, and recovery; (2) technological, ethical, and institutional challenges limiting its adoption in public policy; and (3) strategic and policy applications of AI in enhancing data-driven governance, institutional resilience, and decision transparency. Results highlighted that AI, through real-time analytics and adaptive learning, significantly improves the accuracy and efficiency of crisis-related policymaking.
Conclusion: Artificial intelligence possesses substantial potential to transform crisis management and public governance. However, realizing this potential requires robust ethical frameworks, advanced data infrastructures, and improved digital literacy among policymakers. Responsible and transparent use of AI can lead to proactive, resilient, and evidence-based governance in the twenty-first century.
Downloads
References
Almeida, F., Silva, M., & Dias, T. (2023). Artificial intelligence in disaster communication: Opportunities and challenges for emergency management. Journal of Risk and Crisis Management, 45(2), 102–118.
Crawford, K., & Paglen, T. (2023). Excavating AI: The politics of images in machine learning training sets. AI & Society, 38(1), 55–73.
Desouza, K. C., Dawson, G. S., & Chenok, D. (2023). Artificial intelligence in government: Applications, challenges, and policy considerations. Government Information Quarterly, 40(2), 101763.
Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Harvard Data Science Review, 4(1), 1–16.
Kim, J., & Kim, S. (2022). AI-based early warning systems for natural disasters: A systematic review. Natural Hazards, 111(3), 2021–2042.
Kim, S., Park, J., & Lee, D. (2023). Digital literacy and AI ethics training for public sector leaders. Public Administration Review, 83(4), 712–725.
Kumar, R., & Singh, M. (2023). Optimization of emergency logistics using artificial intelligence: A review. Safety Science, 157, 105958.
Mikhaylov, S. J., Esteve, M., & Campion, A. (2022). Artificial intelligence for the public sector: Opportunities and challenges of cross-sector collaboration. Government Information Quarterly, 39(3), 101702.
Nguyen, T. T., Pham, Q., & Tran, N. (2023). Infrastructure challenges for AI adoption in developing countries. Journal of Information Systems, 37(1), 24–39.
Rahwan, I., Cebrian, M., & Schulte, O. (2022). Machine behaviour: A new frontier for AI research. Nature, 600(7889), 477–486.
Raimo, N., De Marco, M., & Vitolla, F. (2024). Artificial intelligence and public decision-making: Enhancing resilience through data-driven governance. Technological Forecasting and Social Change, 198, 122–136.
Sutton, R., Pidgeon, N., & Thomas, M. (2024). Integrating AI into post-crisis recovery planning: Emerging frameworks and challenges. Safety Science, 160, 106106.
Taddeo, M., & Floridi, L. (2021). How AI can be a force for good in crisis management. Philosophy & Technology, 34(1), 1–10.
Wang, C., Li, Y., & Zhang, P. (2023). Big data and AI for disaster risk reduction: A review. Progress in Disaster Science, 16, 100281.
Wirtz, B. W., & Müller, W. M. (2022). An integrated artificial intelligence framework for public administration. International Review of Administrative Sciences, 88(4), 805–823.
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2022). Artificial intelligence and the public sector: Transforming organizational capabilities. Public Management Review, 24(6), 909–934.
