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Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives
Kwok Pan Chun, Thanti Octavianti, Nilay Doğulu, Hristos Tyralis, Georgia Papacharalampous, Ryan Max Rowberry, Ping Yu Fan, Mark Everard et al.. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 2025-04-14
Topic: Risk, prevention and care in population studies
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. It is essential to explore how AI enhances understanding of legal frameworks and environmental management, while also examining how legal and environmental factors may limit AI's role in society. 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. 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 emphasize 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. Although emerging AI approaches can be powerful tools for disaster management, they must be implemented with ethical considerations and safeguards to address concerns about bias, transparency, and privacy. The responsible execution of AI approaches, based on the dynamic interplay between AI, law, and environmental risk, promotes sustainable and equitable practices in data mining.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Chun, K. P., Octavianti, T., Doğulu, N., Tyralis, H., Papacharalampous, G., Rowberry, R. M., et al. (2025). Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.70011
Vancouver
Chun KP, Octavianti T, Doğulu N, Tyralis H, Papacharalampous G, Rowberry RM, et al. Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 2025. doi:10.1002/widm.70011.
BibTeX
@article{chun2025-1d5cf4b0,
author = {Chun, Kwok Pan and Octavianti, Thanti and Doğulu, Nilay and Tyralis, Hristos and Papacharalampous, Georgia and Rowberry, Ryan Max and Fan, Ping Yu and Everard, Mark and others},
title = {{Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives}},
journal = {Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery},
year = {2025},
doi = {10.1002/widm.70011},
url = {https://doi.org/10.1002/widm.70011}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- 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 emphasize 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.
- Although emerging AI approaches can be powerful tools for disaster management, they must be implemented with ethical considerations and safeguards to address concerns about bias, transparency, and privacy.
- The responsible execution of AI approaches, based on the dynamic interplay between AI, law, and environmental risk, promotes sustainable and equitable practices in data mining.
Why it matters
This paper is about disaster risk reduction, AI, big data, and law, and contains no information on Alzheimer's disease treatment, cure, or prevention.
New to this subject? Start with our plain-English guide: Prevention →
Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery) |
| Journal's citation record | 1 / 4 | Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery: journal h-index 97 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 18 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 19.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 18 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Cheng Li: h-index 32 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Birmingham: institution h-index 1,266 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 16 institution(s) in 10 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 43 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1002/widm.70011
- Original source →
- Free full text: Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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