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

• DOI: 10.48550/arxiv.2410.07123
• Publication Date: 2024-09-20
• Authors: Kwok Pan Chun; Thanti Octavianti; Nilay Doğulu; Hristos Tyralis; Georgia Papachara...; Ryan Rowberry; Ping Yu Fan; Mark Everard; Maria Francesch‐H...; Wellington Migliari; David M. Hannah; John Travis Marshall; Rafael Tolosana C...; Chad Staddon; Ida Ansharyani; Bastien Dieppois; Todd R. Lewis; Juli Ponce Solé; Silvia Ibrean; Tiago Miguel Ferr...; Chinkie Peliño‐Golle; Ye Mu; Manuel Delgado; Eduardo Espinoza; Martin Keulertz; Deepak Gopinath; Cheng Li
• Keywords: Disaster risk reduction

Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of risk reduction. This enables adaptive systems the rapid evolution AI technology, which has significantly impacted intersection law environments. Exploring how influences legal frameworks environmental management, while also examining considerations can confine within socioeconomic domain, essential. From a co-production review perspective, drawing on insights from lawyers, scientists, principles responsible data mining are proposed based safety, transparency, fairness, accountability, contestability. discussion offers blueprint collaboration to create integration knowledge sciences. Discrepancies in use language scientists decision-makers terms usefulness accuracy hamper be used safe, trustworthy, contestable management framework. When networks useful mitigating AI, implications related privacy liability outcomes must considered. Fair accountable emphasise foster discussions public engagement. an important role play education, bringing together next generations work solutions harmony.