ArticleiScience2026
The Black Mirror of AI-driven vulnerability, hazard, and risk research.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As artificial intelligence (AI) is increasingly integrated into disaster risk reduction/management (DRR/M), critical scrutiny remains essential to avert unintended consequences that may compound existing challenges. This study aims to explore the use of AI in hazard, vulnerability, and risk assessments through the analytical lens of the Black Mirror conceptual framework, focusing on (1) the critical shortcomings of these integrations and (2) the potential dangers of translating faulty research outputs into DRR/DRM policy and practices. A semi-systematic review of the literature underpins these identified limitations. While observable in AI applications, the presented aspects are not confined to them. To address the identified concerns, we outline 5 rules of thumb that create a roadmap for improved AI development and integration. We contend that progress in DRR and DRM will be determined less by the sophistication of AI models and more by the intellectual and ethical rigor we apply in their development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.