Transforming Disaster Risk Reduction With <scp>AI</scp> and Big Data: Legal and Interdisciplinary Perspectives
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.
Authors
- David M. Hannah (ORCID: https://orcid.org/0000-0003-1714-1240)
- Ye Mu (ORCID: https://orcid.org/0000-0002-7857-5707)
- Hristos Tyralis (ORCID: https://orcid.org/0000-0002-8932-4997)
- Kwok Pan Chun (ORCID: https://orcid.org/0000-0001-9873-6240)
- Ping Yu Fan (ORCID: https://orcid.org/0000-0003-3618-6639)
- Bastien Dieppois (ORCID: https://orcid.org/0000-0001-7052-1483)
- Todd R. Lewis (ORCID: https://orcid.org/0000-0001-5433-8777)
- Thanti Octavianti (ORCID: https://orcid.org/0000-0002-9921-0539)
- Cheng Li (ORCID: https://orcid.org/0000-0002-4080-8886)
- Tiago Miguel Ferreira (ORCID: https://orcid.org/0000-0001-6454-7927)
- Rafael Tolosana Calasanz
- Georgia Papacharalampous (ORCID: https://orcid.org/0000-0001-5446-954X)
- Maria Francesch‐Huidobro
- Chad Staddon (ORCID: https://orcid.org/0000-0002-2063-8525)
- Nilay Doğulu (ORCID: https://orcid.org/0000-0003-4229-2788)
- Wellington Migliari (ORCID: https://orcid.org/0000-0002-1073-1312)
- E. Silvestre
- Juli Ponce Solé (ORCID: https://orcid.org/0000-0002-1977-5063)
- Mark Everard (ORCID: https://orcid.org/0000-0002-0251-8482)
- Ryan Rowberry (ORCID: https://orcid.org/0000-0002-1888-8867)
- Deepak Gopinath (ORCID: https://orcid.org/0000-0001-5962-6153)
- John Travis Marshall (ORCID: https://orcid.org/0000-0003-0016-2125)
- Manuel Davila Delgado
- Ida Ansharyani
- Silvia Ibrean (ORCID: https://orcid.org/0009-0004-3553-0041)
- Chinkie Peliño‐Golle
- Martin Keulertz
Institutions
- Birmingham City University (GB)
- World Meteorological Organization (CH)
- University of Padua (IT)
- University of California, Santa Barbara (US)
- University of the West of England (GB)
- Georgia State University (US)
- Universidad de Zaragoza (ES)
- Hellenic Air Force (GR)
- Universitas Samawa (ID)
- Hong Kong Design Centre (CN)
- Institute for Sustainability (GB)
- United Nations University (DE)
- Universitat de Barcelona (ES)
- Coventry University (GB)
- Yangzhou University (CN)
- University of Birmingham (GB)
- University of Hong Kong (HK)
- American University of Beirut (LB)
Publication Details
- Journal
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1002/widm.70011
- Citations
- 3
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 7.10
Funders
- Royal Society
- University of the West of England
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- National Natural Science Foundation of China
- Gobierno de Aragón
- National Science and Technology Council
- Natural Environment Research Council
- Agencia Estatal de Investigación