TY - JOUR
T1 - Transforming Disaster Risk Reduction with AI and Big Data
T2 - Legal and Interdisciplinary Perspectives
AU - Chun, Kwok Pan
AU - Octavianti, Thanti
AU - Dogulu, Nilay
AU - Tyralis, Hristos
AU - Papacharalampous, Georgia
AU - Rowberry, Ryan
AU - Everard, Mark
AU - Francesch-Huidobro, Maria
AU - Migliari, Wellington
AU - Hannah, David M.
AU - Marshall, John Travis
AU - Tolosana Calasanz, Rafael
AU - Staddon, Chad
AU - Dieppois, Bastien
AU - Ansharyani, Ida
AU - Lewis, Todd
AU - Ponce, Juli
AU - Ibrean, Silvia
AU - Ferreira, Tiago Miguel
AU - Pelino-Golle, Chinkie
AU - Mu, Ye
AU - Delgado, Manuela Davila
AU - Espinoza, Elizabeth Silvestra
AU - Keulertz, Martin
AU - Gopinath, Deepak
AU - Li, Cheng
N1 - This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
PY - 2025/6
Y1 - 2025/6
N2 - 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 the 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 emphasise 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.
AB - 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 the 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 emphasise 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.
KW - Disaster risk reduction
KW - Artificial Intelligence
KW - Interdisciplinary
KW - Law
KW - public engagement
UR - https://www.scopus.com/pages/publications/105003256744
U2 - 10.1002/widm.70011
DO - 10.1002/widm.70011
M3 - Article
SN - 1942-4795
VL - 15
JO - WIREs Data Mining and Knowledge Discovery
JF - WIREs Data Mining and Knowledge Discovery
IS - 2
M1 - e70011
ER -