Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives
<|reference_start|>Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives: 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.<|reference_end|>
@article{chun2024transforming,
    title={Transforming disaster risk reduction with AI and big data: Legal and
  interdisciplinary perspectives},
    author={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 Peli~no-Golle, Ye Mu, Manuel Delgado, Elizabeth Silvestre Espinoza,
  Martin Keulertz, Deepak Gopinath, Cheng Li},
    journal={arXiv preprint arXiv:2410.07123},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07123},
    primaryClass={cs.CY cs.LG}
}