Document Type

Conference Proceeding

Publication Date

6-12-2025

Publication Title

UMAP Adjunct '25: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

First Page

393

Last Page

397

DOI

https://doi.org/10.1145/3708319.373380

Abstract

Tribal communities face unique challenges in disaster response, often lacking resources and infrastructure to effectively respond to emergencies. This study explores the potential of generative Artificial Intelligence (AI) to enhance disaster response within these communities. We designed a multi-modality generative AI system for disaster assessment from user-generated photos and organized reports with community in-kind cost sharing. We introduced the system prototype at the 2024 National Congress of American Indians (NCAI) conference with emergency department professionals from diverse tribal nations and other stakeholders. Through a workshop-focused group discussion, we discussed the perception, ideas, and concerns for introducing generative AI technology to tribal communities to increase disaster resilience. Our findings suggest considerations about developing strategies and possible governance models when introducing LLM-based models to marginalized local communities with limited resources. This research contributes to literature of the potential and limitations of AI in supporting disaster preparedness and response within indigenous communities, ultimately informing strategies for enhanced tribal disaster resilience and sustainable development goals.

Comments

© {Author | ACM} {2025}. This is the author's accepted version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UMAP Adjunct '25: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization and can be accessed at  https://doi.org/10.1145/3708319.3733806

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