Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives

Publication Title

arXiv

Authors

Kwok P. Chun, University of the West of England, BristolFollow
Thanti Octavianti, University of the West of England, BristolFollow
Nilay Dogulu, World Meteorological OrganizationFollow
Hristos Tyralis, Hellenic Air ForceFollow
Georgia Papacharalampous, National Technical University of AthensFollow
Ryan Rowberry, Georgia State University College of LawFollow
Pingyu Fan, Hong Kong Baptist UniversityFollow
Mark Everard, University of the West of England, BristolFollow
Mira Francesch-Huidobro, The University of Hong KongFollow
Wellington Migliari, University of BarcelonaFollow
David M. Hannah, University of BirminghamFollow
John Travis Marshall, Georgia State University College of LawFollow
Rafael Tolosana Calasanz, Universidad de ZaragozaFollow
Chad Staddon, University of the West of England, BristolFollow
Ida Ansharyani, Universitas SamawaFollow
Bastien Dieppois, Coventry UniversityFollow
Todd R. Lewis, University of the West of England, BristolFollow
Juli Ponce, University of BarcelonaFollow
Silvia Ibrean, United Nations VolunteersFollow
Tiago Miguel Ferreira, University of the West of England, BristolFollow
Chinkie Peliño-Golle, EcoWaste CoalitionFollow
Ye Mu, University of California, Santa BarbaraFollow
Manuel Delgado, University of the West of England, BristolFollow
Elizabeth Silvestre Espinoza, InclimaFollow
Martin Keulertz, University of the West of England, BristolFollow
Deepak Gopinath, University of the West of England, BristolFollow
Cheng Li, Yangzhou UniversityFollow

Document Type

Unpublished Paper

Publication Date

9-2024

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. This enables adaptive systems for the rapid evolution of AI technology, which has significantly impacted the intersection of law and natural environments. Exploring how AI influences legal frameworks and environmental management, while also examining how legal and environmental considerations can confine AI within the socioeconomic domain, is essential.

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. Discrepancies in the use of language between environmental scientists and decision-makers in terms of usefulness and accuracy hamper how AI can be used based on the principles of legal considerations for a safe, trustworthy, and contestable disaster management framework.
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.

Recommended Citation

Kwok P. Chun, Thanti Octavianti, Nilay Dogulu, Hristos Tyralis, Georgia Papacharalampous, Ryan Rowberry, Pingyu Fan, Mark Everard, Maria Francesch-Huidobro, Wellington Migliari, David M. Hannah, John Travis Marshall, Rafael Tolosana Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R. Lewis, Juli Ponce, Silvia Ibrean, Tiago Miguel Ferreira, Chinkie Peliño-Golle, Ye Mu, Manuel Delgado, Elizabeth Silvestre Espinoza, Martin Keulertz, Deepak Gopinath, & Cheng Li, Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives, arXiv (Sept. 20, 2024), https://doi.org/10.48550/arXiv.2410.07123.

Institutional Repository Citation

Kwok P. Chun, Thanti Octavianti, Nilay Dogulu, Hristos Tyralis, Georgia Papacharalampous, Ryan Rowberry, Pingyu Fan, Mark Everard, Mira Francesch-Huidobro, Wellington Migliari, David M. Hannah, John T. Marshall, Rafael T. Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R. Lewis, Juli Ponce, Silvia Ibrean, Tiago M. Ferreira, Chinkie Peliño-Golle, Ye Mu, Manuel Delgado, Elizabeth S. Espinoza, Martin Keulertz, Deepak Gopinath & Cheng Li, Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives, Faculty Publications By Year 3667 (2024)
https://readingroom.law.gsu.edu/faculty_pub/3667

DOI

10.48550/arXiv.2410.07123