Incorporating indigenous knowledge systems into AI governance: enhancing ethical frameworks with Maori and Navajo perspectives

This paper addresses a significant gap in AI governance by examining the limitations of Western-centric ethical frameworks through a comparative analysis of Māori Kaitiakitanga and Navajo Hózhó principles. While prior research emphasizes the role of Indigenous knowledge in AI, this study provides a concrete framework for integrating these principles into AI governance. The study proposes culturally relevant AI governance frameworks by highlighting the unique contributions of each Indigenous knowledge system and identifying common ground. Additionally, it examines the synergies and tensions between Indigenous values and principal AI ethical frameworks—such as fairness, accountability, and transparency—resulting in targeted recommendations for incorporating Indigenous perspectives into AI governance. This research aims to cultivate a more inclusive and equitable AI landscape that honors the rights and values of Indigenous peoples while leveraging their insights to address ethical dilemmas in AI governance. Additionally, this paper demonstrates how these ethical principles translate to practical requirements for individual AI projects, showing that IKS integration prevents project failure and ensures operational viability in Indigenous contexts. The analysis juxtaposes Indigenous perspectives on collective data ownership, ecological stewardship, and relational accountability against prevailing AI ethical norms, uncovering critical shortcomings in current frameworks regarding Indigenous priorities. It draws on specific examples from Māori environmental management and Navajo health initiatives. This research illustrates how Kaitiakitanga and Hózhó can enhance data sovereignty, ecological sustainability, and community-driven decision-making in AI governance. Suggested strategies include mandated Indigenous representation in AI policymaking, culturally appropriate data governance protocols, and community-led impact assessment frameworks.