Artificial Intelligence and Big Data in Disaster Risk Management from a Socio-Legal Perspective
DOI:
https://doi.org/10.54938/ijemdss.2026.05.2.682Keywords:
disaster risk reduction, artificial intelligence, law, public participationAbstract
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 significantly impacted where law and the natural environment intersect, influencing legal systems and environmental policies, and how legal and environmental issues can prevent AI from having a greater impact on society and the economy. As part of a participatory assessment of production, with the assistance of legal experts, social and environmentalists, the key principles of responsible data analysis are proposed. These principles focus on security, transparency, fairness, accountability, and the ability to challenge or challenge decisions. This conversation describes how different disciplines can work together to create flexible legal systems that use AI, while drawing knowledge from the environmental and social sciences. The way environmentalists and decision-makers talk about useful and accurate information leads to differences that make the use of artificial intelligence difficult due to legal issues related to the reliability, reliability, and contentiousness of disaster management systems. If social media is useful for disaster risk reduction through AI, it's important to consider legal issues related to who is responsible and how sensitive the information used in disaster management is. Ideas for a fair and responsible process focus on the environment and encourage discussions on social and economic issues related to public participation. AI is also very important in education, as it brings together the next generation of law, social and natural sciences to jointly find solutions that bring together different disciplines in a balanced way. AI tools can be very useful in emergency management, but it's important to use them fairly and responsibly. You need to think about things like possible unfair benefits, clear explanations, and protecting people's personal data. Careful use of AI techniques, considering how AI, laws, and environmental risks interact with each other, helps create equitable and sustainable ways to collect and use data.
Downloads
References
AIHLEG. (2019). Ethics guidelines for trustworthy AI. European Commission, High-Level Expert Group on Artificial Intelligence. https://www.europarl.europa.eu/cmsdata/196377/AI%20HLEG_Ethics%20Guide lines%20for%20Trustworthy%20AI.pdf
Albert, E. T. (2019). AI in talent acquisition: a review of AI-applications used in recruitment and selection. Strategic HR Review, 18(5), 215-221. https://doi.org/10.1108/SHR-04-2019-0024
Alizadeh, B., Li, D., Hillin, J., Meyer, M.A., Thompson, C.M., Zhang, Z. and Behzadan, A.H., (2022). Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos. Advanced Engineering Informatics, 54, p.101730. https://doi-org/ 10.1016/j.aei.2022.101730
Almada, M. (2019, June). Human intervention in automated decision-making: Toward the construction of contestable systems. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law (pp. 2-11). https://doi.org/10.1145/3322640.3326699
Baker, J., Cerniglia, C., Finger, D., E. Herrera, L., & Newman, J. (2022). Creating Blueprints for Law School Responses to Natural Disasters. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 389-407). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.025
Berber, A., Srećković, S. (2023) When something goes wrong: Who is responsible for errors in ML decision-making?. AI and Society. https://doi.org/10.1007/s00146-023-01640-1.
Carlarne, C. (2022). From Covid-19 to Climate Change: Disaster and Inequality at the Crossroads. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 511-524). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.033
Chauhan, P., Akiner, M.E., Shaw, R. & Sain, K. (2024) Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: using Markov Chain and LSTM approaches. Artificial Intelligence in GeoSciences, 5, 100069.
Chen, N., Liu, W., Bai, R. & Chen, A. (2019). Application of computational intelligence technologies in emergency management: a literature review. Artificial Intelligence Review, 52, pp.2131-2168. https://doi.org/10.1007/s10462-017-9589-8
Coeckelbergh, M. (2019). Artificial intelligence: some ethical issues and regulatory challenges. Technology and regulation, 2019, 31-34.
https://doi.org/10.26116/techreg.2019.003
Costanza, R., Fath, B., Fu, B., Hastings, A., Larry Li, B., Mackey, B., Meynecke, O., Maloney, M., Mitsch, W. J., Ouyang, Z., Petrovskiy, S., Stokes, A., Thinley, J., &Zhiyun, O. (2023). EcoSummit 2023 Conference Declaration: Building a Sustainable Wellbeing Future. Ecological Engineering 194, 107052.
https://doi.org/10.1016/j.ecoleng.2023.107052
Cutter, S. (2018). Compound Cascading, or Complex Disasters: What’s in a Name? Environment: Science and Policy for Sustainable Development 60, 16–25. https://doi.org/10.1080/00139157.2018.1517518
Cutter, S. (2022). Governance Structures for Recovery and Resilience. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 59-70). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.004
Deltares, WB & GFDRR. (2021). Responsible artificial intelligence for disaster risk management: working group summary. 44 pages. Available at: https://reliefweb.int/report/world/responsible-ai-disaster-risk-management-working-group-summary (the World Bank, the Global Facility for Disaster Reduction and Recovery, the Deltares)
DSIT. (2023). A pro-innovation approach to AI regulation. UK Department for Science, Innovation and Technology. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/at tachment_data/file/1146542/a_pro-innovation_approach_to_AI_regulation.pdf
Duan, Y, Edwards, J S, & Dwivedi Y K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48:63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021.
EC. (2023). Regulatory framework proposal on artificial intelligence. European Commission. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
Fan, P., & Chun, K. (2022). An Adaptive Legal Framework for Water Security Concerns in the Guangdong-Hong Kong-Macao Greater Bay Area. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 131-148). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.009
Francesch-Huidobro, M. (2022). Climate Resilience in the Greater Bay Area of South China. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks,
pp. 107-130). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.008
Frantz, P., & Instefjord, N. (2018). Regulatory competition and rules/principles-based regulation. Journal of Business Finance & Accounting, 45(7-8), 818-838.
Finn, D. & Marshall, J.T. (2018). Superstorm Sandy at Five: Lessons on Law as Catalyst and Obstacle to Long-Term Recovery Following Catastrophic Disaster, Environmental Law Reporter News & Analysis, 48, pp. 10494 - 10519.
Gable, L. (2022). Disasters and Disability. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 525-540). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.034
Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021). Fairness and accountability of AI in disaster risk management: Opportunities and challenges. Patterns, 2(11). https://doi.org/10.1016/j.patter.2021.100363
Ghani, N.A., Hamid, S., Hashem, I.A.T. & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417-428. https://doi.org/10.1016/j.chb.2018.08.039
Gill, B. Global Climate Emergency: after COP24, climate science, urgency, and the threat to humanity. Economics and Climate Emergency, 17(6), 885-902.
https://doi.org/10.1080/14747731.2019.1669915
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660.
https://doi.org/10.5465/annals.2018.0057
Gneiting, T., & Raftery, A.E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102(477):359–378. https://doi.org/10.1198/016214506000001437.
Goodman, K., Zandi, D., Reis, A., & Vayena, E. (2020). Balancing risks and benefits of artificial intelligence in the health sector. Bulletin of the World Health Organization, 98(4), 230. https://doi.org/10.2471%2FBLT.20.253823
Government of India. (2005) Disaster Management Act, 2005. Legislative Department. Available at:
https://cdn.s3waas.gov.in/s365658fde58ab3c2b6e5132a39fae7cb9/uploads/201 8/04/2018041720.pdf
Guikema, S. (2020) Artificial intelligence for natural hazards risk analysis: potential, challenges and research needs. Risk Analysis, 40(6), pp.1117-1123. https://doi.org/10.1111/risa.13476
Gunes Peschke, S., & Peschke, L. (2022). Artificial Intelligence and the New Challenges for EU Legislation. YBHD, 1267.
Hoofnagle, C. J., Van Der Sloot, B., & Borgesius, F. Z. (2019). The European Union general data protection regulation: what it is and what it means. Information & Communications Technology Law 28(1):65−98. https://doi.org/10.1080/13600834.2019.1573501
IEEE (2019) Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems (A/IS). The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. Accessed from https://standards.ieee.org/wp-
content/uploads/import/documents/other/ead_v2.pdf
Imazon (2023). PrevisIA: principais resultados. Instituto do Homem e do Meio Ambiente da Amazo^ nia. 110 pages. Available at: https://previsia.org.br/wp-content/uploads/2023/04/PrevisIA-Principais-resultados-2023.pdf
Imran, M., Ofli, M., Caragea, D. & Torralba, A. (2020). Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities Challenges, and Future Directions. Information Processing & Management 57, 102261.
https://doi.org/10.1016/j.ipm.2020.102261
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer, New York. https://doi.org/10.1007/978-1-4614-7138-7.
Kahneman. D., Tversky, A. (1979). Prospect theory: An analysis of decision under risk.
Econometrica 47(2):263–292.
Kuo, S, Marshall, J., & Rowberry, R. (Eds.). (2022) The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108770903.001
Lamberton, C., Brigo, D., & Hoy, D. (2017). Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities. Journal of Financial Perspectives, 4(1).
Lehto, M. (2022). Cyber-attacks against critical infrastructure. In Cyber Security: Critical Infrastructure Protection (pp. 3-42). Cham: Springer International Publishing.
Lieske, S. N., Leao, S. Z., Conrow, L., & Pettit, C. (2021). Assessing geographical representativeness of crowdsourced urban mobility data: An empirical investigation of Australian bicycling. Environment and Planning B: Urban Analytics and City Science, 48(4), 775–792. https://doi.org/10.1177/2399808319894334
Lyons, H., Velloso, E., & Miller, T. (2021). Conceptualising contestability: Perspectives on contesting algorithmic decisions. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-25. https://doi.org/10.1145/3449180
Madan, A., Routray, J. K. (2015). Institutional framework for preparedness and response of disaster management institutions from national to local level in India with focus on Delhi. International Journal of Disaster Risk Reduction, 14:545−555. https://doi.org/10.1016/j.ijdrr.2015.10.004
Matsuda, M. (2022). The Flood: Political Economy and Disaster. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 48-56). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.003
Marshall, J.T., (2015). Rating the Cities: Constructing a City Resilience Index for Assessing the Effect of State and Local Laws on Long-Term Recovery from Crisis and Disaster. Tulane Law Review, 90, pp. 35 - 74.
Mohanan, C., Menon, V. (2016). Disaster management in India—An analysis using COBIT 5 principles. In: 2016 IEEE Global Humanitarian Technology Conference (GHTC, pp. 209-212. https://doi.org/10.1109/GHTC.2016.7857282
Morales, L. G., Hsu, Y., Poole, J., Rae, B., & Rutherford, I. (2014). A world that counts: Mobilising the data revolution for sustainable development (Report No. 1). United Nations. https://www.undatarevolution.org/wp-content/uploads/2014/11/A-World-That-Counts.pdf
Nemakonde, D L., Van Niekerk, D. (2022). Integrating Disaster Risk Reduction and Climate Change Adaptation in the Context of Sustainable Development in Africa. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 95-106). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.007
Olterman, P. (2021, July 19). German flood alert system criticised for ‘monumental failure’. The Guardian. https://www.theguardian.com/world/2021/jul/19/german-villages-could-be-left-with-no-drinking-water-after-floods
Papadakis, E., Adams, B., Gao, S., Martins, B., Baryannis, G. and Ristea, A. (2022). Explainable artificial intelligence in the spatial domain (X-GeoAI). Transactions in GIS, 26(6). doi: 10.1111/tgis.12996
Pelin˜ o-Golle, C., & Baula, F. (2022). Averting Disasters through Watershed Policy Advocacy: The Case of the Philippines’ Largest Highly Urbanized City. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 353-365). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.022
Rowberry, R. (2022). Reflections on Urban Cultural Heritage, Public Health, and Public Participation. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 468-476). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.030
Seamans, R. (2023, July 27). AI regulation is coming to the US, albeit slowly. Forbes. https://www.forbes.com/sites/washingtonbytes/2023/06/27/ai-regulation-is-coming-to-the-us-albeit-slowly/?sh=14d601367ee1
Seneviratne, K., Baldry, D., & Pathirage, C. (2010). Disaster Knowledge Factors in Managing Disasters Successfully. International Journal of Strategic Property Management 14, 376–390. https://doi.org/10.3846/ijspm.2010.28
Shakeri, E., Vizvari, B., Nazerian, R. (2021). Comparative analysis of disaster management between India and Nigeria. International Journal of Disaster Risk Reduction, 63:102448. https://doi.org/10.1016/j.ijdrr.2021.102448
Sherwin, B. (2022). After the Storm: The Importance of Acknowledging Environmental Justice in Sustainable Development and Disaster Preparedness. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 479-496). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.031
Siau, K. & Wang, W., (2020). Artificial intelligence (AI) ethics: ethics of AI and ethical AI. Journal of Database Management (JDM), 31(2), pp.74-87. https://doi.org/10.4018/JDM.2020040105
Smuha, N. A. (2021). From a ‘race to AI’ to a ‘race to AI regulation’: regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57-84. https://dx.doi.org/10.2139/ssrn.3501410
Soylu, A., Corcho, O' ., Elvesæter, B., Badenes-Olmedo, C., Yedro-Martí´nez, F., Kovacic, M., & Taggart, C. (2022). Data quality barriers for transparency in public procurement. Information 13(2):1-21. https://doi.org/10.3390/info13020099
Sun, W., Bocchini, P. & Davison, B.D. (2020). Applications of artificial intelligence for disaster management. Natural Hazards, 103(3), pp.2631-2689. https://doi.org/10.1007/s11069-020-04124-3
Thieken, A. H., Bubeck, P., Heidenreich, A., von Keyserlingk, J., Dillenardt, L., & Otto, A.: Performance of the flood warning system in Germany in July 2021 – insights from affected residents, Nat. Hazards Earth Syst. Sci., 23, 973–990, https://doi.org/10.5194/nhess-23-973-2023, 2023.
Tikkinen-Piri, C., Rohunen, A., & Markkula, J. (2018). EU General Data Protection Regulation: Changes and implications for personal data collecting companies. Computer Law and Security Review 34(1):134−153. https://doi.org/10.1016/j.clsr.2017.05.015
Trustible. (2023, July 18). How does China’s approach to AI regulation differ from the US And EU? Forbes. https://www.forbes.com/sites/forbeseq/2023/07/18/how-does-chinas-approach-to-ai-regulation-differ-from-the-us-and-eu/?sh=5e4e44f351c6
Ufert, F. (2020). AI Regulation Through the Lens of Fundamental Rights: How Well Does the GDPR Address the Challenges Posed by AI? European Papers. 2020. https://search.datacite.org/works/10.15166/2499-8249/394
Villa, C. (2022). Law and Lawyers in Disaster Response. In S. Kuo, J. Marshall, & R. Rowberry (Eds.), The Cambridge Handbook of Disaster Law and Policy: Risk, Recovery, and Redevelopment (Cambridge Law Handbooks, pp. 408-420). Cambridge: Cambridge University Press. doi:10.1017/9781108770903.026
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 1-10. https://doi.org/10.1038/s41467-019-14108-y
Wiegmann, M., Kersten, J., Senaratne, H., Potthast, M., Klan, F., & Stein, B. (2021) Opportunities and risks of disaster data from social media: a systematic review of incident information. Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021
Yu, M., Yang, C. & Li, Y., (2018). Big data in natural disaster management: A review.
Geosciences, 8(5), p.165. https://doi.org/10.3390/geosciences8050165
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Hafeez-ur-Rehman

This work is licensed under a Creative Commons Attribution 4.0 International License.
Under the Creative Common Attribution (CC-BY 4.0) license, authors retain copyright and grant the journal right of first publication.






