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Kaitiakitanga (Māori Guardianship and Active Protection) and Environmental Governance in the Age of AI: A study of AI governance in Aotearoa New Zealand

Wu, Songze LU (2026) In Master Thesis Series in Environmental Studies and Sustainability Science MESM02 20261
LUCSUS (Lund University Centre for Sustainability Studies)
Abstract
While AI can offer powerful predictive capabilities to facilitate environmental management, its rapid and often uncritical adoption risks instigating a new wave of "digital colonialism" by commodifying Indigenous environmental data and traditional knowledge. Drawing on the Governance Analytical Framework, Marxist political ecology, and decolonial theory, I examine current international and domestic AI governance and three applied AI case studies in climate adaptation and conservation in Aotearoa New Zealand. My findings highlight significant structural friction between state-mandated, individualistic data protection frameworks and the collective, sovereignty-based proposals of the Māori Data Governance Model and the CARE Principles for... (More)
While AI can offer powerful predictive capabilities to facilitate environmental management, its rapid and often uncritical adoption risks instigating a new wave of "digital colonialism" by commodifying Indigenous environmental data and traditional knowledge. Drawing on the Governance Analytical Framework, Marxist political ecology, and decolonial theory, I examine current international and domestic AI governance and three applied AI case studies in climate adaptation and conservation in Aotearoa New Zealand. My findings highlight significant structural friction between state-mandated, individualistic data protection frameworks and the collective, sovereignty-based proposals of the Māori Data Governance Model and the CARE Principles for Indigenous Data Governance. To counter extractive data practices and the looming socio-economic risks of techno-feudalism, the study offers three key takeaways for future AI governance: increase wider governance participation and actors’ accountabilities; clarify actors’ responsibilities and reserve the right to protective actions; and deepen understanding of AI risks and trade-offs for continuous management. (Less)
Please use this url to cite or link to this publication:
author
Wu, Songze LU
supervisor
organization
course
MESM02 20261
year
type
H2 - Master's Degree (Two Years)
subject
keywords
AI Governance, Environmental Governance, Climate Adaptation, Conservation, Māori Data Governance, Indigenous Knowledge, Sustainability Science
publication/series
Master Thesis Series in Environmental Studies and Sustainability Science
report number
2026:039
language
English
id
9252877
date added to LUP
2026-10-26 10:21:14
date last changed
2026-10-26 10:21:14
@misc{9252877,
  abstract     = {{While AI can offer powerful predictive capabilities to facilitate environmental management, its rapid and often uncritical adoption risks instigating a new wave of "digital colonialism" by commodifying Indigenous environmental data and traditional knowledge. Drawing on the Governance Analytical Framework, Marxist political ecology, and decolonial theory, I examine current international and domestic AI governance and three applied AI case studies in climate adaptation and conservation in Aotearoa New Zealand. My findings highlight significant structural friction between state-mandated, individualistic data protection frameworks and the collective, sovereignty-based proposals of the Māori Data Governance Model and the CARE Principles for Indigenous Data Governance. To counter extractive data practices and the looming socio-economic risks of techno-feudalism, the study offers three key takeaways for future AI governance: increase wider governance participation and actors’ accountabilities; clarify actors’ responsibilities and reserve the right to protective actions; and deepen understanding of AI risks and trade-offs for continuous management.}},
  author       = {{Wu, Songze}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master Thesis Series in Environmental Studies and Sustainability Science}},
  title        = {{Kaitiakitanga (Māori Guardianship and Active Protection) and Environmental Governance in the Age of AI: A study of AI governance in Aotearoa New Zealand}},
  year         = {{2026}},
}