Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives

• Published in: arXiv.org
• Main Authors: Chun, Kwok P, Octavianti, Thanti, Dogulu, Nilay, Tyralis, Hristos, Papacharalampous, Georgia, Ryan Rowberry, Fan, Pingyu, Everard, Mark, Francesch-Huidobro, Maria, Migliari, Wellington, Hannah, David M, John Travis Marshall, Rafael Tolosana Calasanz, Staddon, Chad, Ansharyani, Ida, Dieppois, Bastien, Lewis, Todd R, Ponce, Juli, Ibrean, Silvia, Tiago Miguel Ferreira, Peliño-Golle, Chinkie, Ye Mu, Delgado, Manuel, Elizabeth Silvestre Espinoza, Keulertz, Martin, Gopinath, Deepak, Cheng, Li
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 20.09.2024
• Subjects: Adaptive systems; Big Data; Data mining; Disaster management; Environmental management; Interdisciplinary aspects; Legislation; Public participation; Risk management; Scientists; Social networks; Social sciences

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. This enables adaptive systems for the rapid evolution of AI technology, which has significantly impacte...