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Purpose

This study aims to explore how artificial intelligence (AI) can ethically and effectively support climate resilience in small island developing states (SIDS). It presents a context-sensitive analysis that integrates ethical AI governance principles into regional adaptation strategies, addressing key structural and systemic barriers such as limited data infrastructure and inequitable access to climate finance.

Design/methodology/approach

The study applies a qualitative thematic analysis, guided by Braun and Clarke’s (2006) six-phase framework, on a curated corpus of ten diverse documents, including peer-reviewed papers, UN policy reports, AI system overviews and technical evaluations of resilience platforms. To validate thematic patterns, natural language processing (NLP) tools such as keyword frequency and bigram analysis were applied.

Findings

Four dominant themes emerged: (1) the necessity of open-access data platforms for regional resilience collaboration; (2) the integration of ethical AI governance frameworks; (3) the critical importance of community-centered adaptation approaches; and (4) persistent barriers in accessing climate finance. The convergence between manual thematic coding and NLP-supported validation reinforced the credibility of the findings.

Research limitations/implications

The study is based solely on document analysis and does not include field-based engagement with SIDS communities. Future research could incorporate participatory methods (e.g. interviews and codesign workshops) or evaluate specific AI-driven initiatives to strengthen contextual validation.

Practical implications

The paper offers actionable guidance for policymakers, including the establishment of regional data-sharing ecosystems, the adoption of ethical AI frameworks, investment in local technical capacity and reforms in international climate finance mechanisms to enhance equity for SIDS.

Originality/value

By integrating ethical governance into AI climate resilience strategies tailored to small island contexts, this study addresses a significant gap in current scholarship and provides a scalable framework that balances technological innovation with environmental justice and regional equity.

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