book chapter
Artificial intelligence can amplify indigenous knowledge systems to build climate resilience when developed through ethical, decolonial frameworks. An evaluation of 45 academic sources alongside interviews with 10 indigenous leaders, technologists, and ethicists highlights five priority areas: participatory design, indigenous data sovereignty, treating artificial intelligence as a knowledge amplifier rather than a replacement, community capacity building, and relational ethics. The research introduces the AI-Indigenous Knowledge Co-Production System framework to integrate indigenous worldviews, sovereignty, and accountability into technical innovation. Without intentional decolonial practices, technological tools risk worsening climate vulnerability and perpetuating epistemic injustice. Practical implementation requires embedding Free, Prior and Informed Consent protocols, establishing culturally sensitive design processes, and creating sustained community partnerships. In addition, policy recommendations urge the formal institutionalisation of indigenous participation in governance forums and the enactment of protective data sovereignty laws.
Indigenous communities hold vital insights for climate resilience, yet emerging digital tools often overlook or exploit these traditions. Integrating indigenous knowledge systems into artificial intelligence design protects community sovereignty while improving climate adaptation strategies. Ensuring technologies respect local consent and governance helps prevent digital harms and fosters more equitable, sustainable environmental solutions.
The research presents an early-stage conceptual framework, the AI-Indigenous Knowledge Co-Production System, rather than a commercial product. Intended for software developers, ethicists, and climate governance organisations, the model guides the creation of culturally sensitive tools using Free, Prior and Informed Consent protocols. Practical application remains distant, as real-world implementation depends on future technology co-design, legal frameworks for indigenous data sovereignty, and extended community capacity-building partnerships.
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The accelerating climate crisis demands innovative, inclusive solutions that transcend technocratic paradigms and centre historically marginalized knowledge systems. This study explores how artificial intelligence (AI), when ethically designed and epistemologically reimagined, can serve as a tool to amplify indigenous knowledge systems (IKS) for climate resilience. Grounded in epistemological pluralism, ethical AI theory and decolonial theory, the research adopts a qualitative exploratory design, utilizing systematic content analysis of 45 scholarly sources and semi-structured interviews with 10 indigenous leaders, technologists and ethicists. Findings reveal five major themes: the necessity of participatory AI design, the critical role of indigenous data sovereignty and ethics, the positioning of AI as a knowledge amplifier rather than a replacement, the importance of community capacity building and the centrality of relational ethics. The study proposes the AI-Indigenous Knowledge Co-Production System (AIK-CPS) model as a transformative framework for embedding indigenous worldviews, relational accountability and sovereignty into AI innovation processes. It argues that without intentional decolonial approaches, AI risks perpetuating epistemic injustice and exacerbating climate vulnerability among indigenous communities. Policy implications include the institutionalization of indigenous participation in AI and climate governance forums and the urgent enactment of indigenous data sovereignty laws. Practically, the study recommends embedding Free, Prior and Informed Consent (FPIC) protocols, fostering culturally sensitive design and cultivating long-term capacity-building partnerships. Repositioning AI as a vehicle for epistemological justice rather than technological colonization allows this research to contribute meaningfully to global efforts aimed at forging sustainable, relational and equitable climate futures.
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DOI: 10.1108/978-1-83742-268-520260015
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