Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| author | Chun, Kwok P Octavianti, Thanti Dogulu, Nilay Tyralis, Hristos Papacharalampous, Georgia Rowberry, Ryan Fan, Pingyu Everard, Mark Francesch-Huidobro, Maria Migliari, Wellington Hannah, David M. Marshall, John Travis Calasanz, Rafael Tolosana Staddon, Chad Ansharyani, Ida Dieppois, Bastien Lewis, Todd R Ponce, Juli Ibrean, Silvia Ferreira, Tiago Miguel Peliño-Golle, Chinkie Mu, Ye Delgado, Manuel Espinoza, Elizabeth Silvestre Keulertz, Martin Gopinath, Deepak Li, Cheng |
| author_facet | Chun, Kwok P Octavianti, Thanti Dogulu, Nilay Tyralis, Hristos Papacharalampous, Georgia Rowberry, Ryan Fan, Pingyu Everard, Mark Francesch-Huidobro, Maria Migliari, Wellington Hannah, David M. Marshall, John Travis Calasanz, Rafael Tolosana Staddon, Chad Ansharyani, Ida Dieppois, Bastien Lewis, Todd R Ponce, Juli Ibrean, Silvia Ferreira, Tiago Miguel Peliño-Golle, Chinkie Mu, Ye Delgado, Manuel Espinoza, Elizabeth Silvestre Keulertz, Martin Gopinath, Deepak Li, Cheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07123 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives Chun, Kwok P Octavianti, Thanti Dogulu, Nilay Tyralis, Hristos Papacharalampous, Georgia Rowberry, Ryan Fan, Pingyu Everard, Mark Francesch-Huidobro, Maria Migliari, Wellington Hannah, David M. Marshall, John Travis Calasanz, Rafael Tolosana Staddon, Chad Ansharyani, Ida Dieppois, Bastien Lewis, Todd R Ponce, Juli Ibrean, Silvia Ferreira, Tiago Miguel Peliño-Golle, Chinkie Mu, Ye Delgado, Manuel Espinoza, Elizabeth Silvestre Keulertz, Martin Gopinath, Deepak Li, Cheng Computers and Society Machine Learning 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. |
| title | Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2410.07123 |