Trustworthy AI for Disaster Anticipation and Humanitarian Response

This interdisciplinary perspective examines trustworthy artificial intelligence for disaster anticipation and humanitarian response. The organizing question is how predictive tools can improve timeliness while preserving local knowledge, accountability, and do-no-harm practice. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to evaluating prediction independently from whether warnings are trusted and actionable. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in multi-hazard early warning and humanitarian logistics.

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