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Cited by 19Ethics and Social Impacts of AIDigitalization, Law, and RegulationArtificial Intelligence in Law

Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives

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Chun, Kwok P., Octavianti, Thanti, Dogulu, Nilay, Tyralis, Hristos, Papacharalampous, Georgia, Rowberry, Ryan, Fan, Pingyu, Everard, Mark +19 more·2025·WIREs Data Mining and Knowledge Discovery
DOI 10.1002/widm.70011
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Abstract

Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. It is essential to explore how AI enhances understanding of legal frameworks and environmental management, while also examining how legal and environmental factors may limit AI's role in society. From a co‐production review perspective, drawing on insights from lawyers, social scientists, and environmental scientists, principles for responsible data mining are proposed based on safety, transparency, fairness, accountability, and contestability. This discussion offers a blueprint for interdisciplinary collaboration to create adaptive law systems based on AI integration of knowledge from environmental and social sciences. When social networks are useful for mitigating disaster risks based on AI, the legal implications related to privacy and liability of the outcomes of disaster management must be considered. Fair and accountable principles emphasize environmental considerations and foster socioeconomic discussions related to public engagement. AI also has an important role to play in education, bringing together the next generations of law, social sciences, and natural sciences to work on interdisciplinary solutions in harmony. Although emerging AI approaches can be powerful tools for disaster management, they must be implemented with ethical considerations and safeguards to address concerns about bias, transparency, and privacy. The responsible execution of AI approaches, based on the dynamic interplay between AI, law, and environmental risk, promotes sustainable and equitable practices in data mining.

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Authors
Chun, K. P., Octavianti, T., Dogulu, N., Tyralis, H., Papacharalampous, G., Rowberry, R., Fan, P., Everard, M., Francesch‐Huidobro, M., Migliari, W., Hannah, D. M., Marshall, J. T., Calasanz, R. T., Staddon, C., Ansharyani, I., Dieppois, B., Lewis, T. R., Ponce, J., Ibrean, S., Ferreira, T. M., Peliño‐Golle, C., Mu, Y., Delgado, M. D., Espinoza, E. S., Keulertz, M., Gopinath, D., & Li, C.
Affiliations
  • University of the West of England (GB)
  • Hydrology, Water Resources and Cryosphere Division World Meteorological Organization (WMO) Geneva Switzerland
  • Hellenic Air Force (GR)
  • University of Padua (IT)
  • Georgia State University (US)
  • Department of Urban Planning and Design The University of Hong Kong Hong Kong Hong Kong
  • University of Hong Kong (HK)
  • Universitat de Barcelona (ES)
  • University of Birmingham (GB)
  • Universidad de Zaragoza (ES)
  • Universitas Samawa (ID)
  • Coventry University (GB)
  • Law School, University of Barcelona Barcelona Catalonia Spain
  • United Nations (US)
  • EcoWaste Coalition Manila Philippines
  • University of California, Santa Barbara (US)
  • Birmingham City Business School Birmingham City University Birmingham UK
  • Inclima Oslo Fornebu Norway
  • American University of Beirut (LB)
  • Yangzhou University (CN)
Corresponding author
  • Chun, Kwok P.
  • Octavianti, Thanti
ORCID
Venue
WIREs Data Mining and Knowledge Discovery
Publisher
Wiley
Published
2025-04-14
Volume
15
Issue
2
ISSN
  • 1942-4787 (ISSN-L)
  • 1942-4795
DOI
10.1002/widm.70011
OpenAlex
W4409464262
Cited by
19
References
7
Citations by year
2026: 11 · 2025: 8
FWCI
19.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion, cc-by)
  • University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
  • BCU Open Access Repository (Birmingham City University) (submittedVersion)
  • Zaguan (University of Zaragoza Repository) (submittedVersion, cc-by)
  • eYLS (Yale Law School) (submittedVersion)
  • Pure (Coventry University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • Royal Society (IEC\NSFC\223132)
  • University of the West of England
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
Keywords
disaster risk reduction, artificial intelligence, big data, interdisciplinary collaboration, legal framework, environmental management, transparency and accountability, privacy, liability, social networks, disaster management, AI ethics, citizen participation, environmental risk

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