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000156681 005__ 20251017144650.0
000156681 0247_ $$2doi$$a10.1002/widm.70011
000156681 0248_ $$2sideral$$a143958
000156681 037__ $$aART-2025-143958
000156681 041__ $$aeng
000156681 100__ $$aChun, Kwok P.
000156681 245__ $$aTransforming disaster risk reduction with AI and Big Data: Legal and interdisciplinary perspectives
000156681 260__ $$c2025
000156681 5060_ $$aAccess copy available to the general public$$fUnrestricted
000156681 5203_ $$aManaging 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.
000156681 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00$$9info:eu-repo/grantAgreement/ES/MCINN/PID2020-113796RB-I00
000156681 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000156681 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000156681 700__ $$aOctavianti, Thanti
000156681 700__ $$aDogulu, Nilay
000156681 700__ $$aTyralis, Hristos
000156681 700__ $$aPapacharalampous, Georgia
000156681 700__ $$aRowberry, Ryan
000156681 700__ $$aFan, Pingyu
000156681 700__ $$aEverard, Mark
000156681 700__ $$aFrancesch-Huidobro, Maria
000156681 700__ $$aMigliari, Wellington
000156681 700__ $$aHannah, David M.
000156681 700__ $$aMarshall, John Travis
000156681 700__ $$0(orcid)0000-0003-3057-6273$$aTolosana Calasanz, Rafael$$uUniversidad de Zaragoza
000156681 700__ $$aStaddon, Chad
000156681 700__ $$aAnsharyani, Ida
000156681 700__ $$aDieppois, Bastien
000156681 700__ $$aLewis, Todd R.
000156681 700__ $$aPonce, Juli
000156681 700__ $$aIbrean, Silvia
000156681 700__ $$aFerreira, Tiago Miguel
000156681 700__ $$aPeliño-Golle, Chinkie
000156681 700__ $$aMu, Ye
000156681 700__ $$aDelgado, Manuel Davila
000156681 700__ $$aEspinoza, Elizabeth Silvestre
000156681 700__ $$aKeulertz, Martin
000156681 700__ $$aGopinath, Deepak
000156681 700__ $$aLi, Cheng
000156681 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000156681 773__ $$g15, 2 (2025), e70011 [11 pp.]$$pWiley interdisciplinary reviews. Data mining and knowledge discovery$$tWiley interdisciplinary reviews. Data mining and knowledge discovery$$x1942-4787
000156681 8564_ $$s820205$$uhttps://zaguan.unizar.es/record/156681/files/texto_completo.pdf$$yVersión publicada
000156681 8564_ $$s2795617$$uhttps://zaguan.unizar.es/record/156681/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000156681 909CO $$ooai:zaguan.unizar.es:156681$$particulos$$pdriver
000156681 951__ $$a2025-10-17-14:36:08
000156681 980__ $$aARTICLE