Leveraging artificial intelligence to democratise access to land and water data in Aotearoa New Zealand: potential and risks
Abstract
Artificial intelligence has the potential to democratise access to land and water data, but only if implemented equitably. This has significant impact on the recognition and empowerment of indigenous communities reinforcing that this issue is part of a broader international debate on digital rights, indigenous sovereignty and democratisation. Taking an Aotearoa/New Zealand-focused approach, the decentralised Resource Management Act (1991) data infrastructure, coupled with the lack of ongoing investment in collections and monitoring programmes to track the changing environment, has brought about a locally fragmented land and water data landscape. Henceforth, we developed a bottom-up methodology informing the development of Kuaha Matihiko (KM), a digital gateway that brings together disparate datasets and provides a user-friendly interface to access high-quality, trustworthy land and water data for diverse actors across private and public spheres. We consider how, with the appropriate developmental support and ethical governance, new technologies like artificial intelligence can enhance efficiency for professionals while supporting democratic, deliberative, multi-stakeholder land and water decision making and support the leadership role that tāngata whenua (indigenous Māori people from Aotearoa/New Zealand) have taken in these spaces. By facilitating a dialogue between indigenous knowledge and “western” scientific approaches, artificial intelligence has the potential to bridge epistemological differences and foster a more collaborative, innovative and inclusive approach to environmental stewardship. Finally, we explore future evolutions for the KM tool to expand its scope and functionality by incorporating other new technologies.
Fields of Study & Subjects
Environmental Monitoring and Data Management
Ethics and Social Impacts of AI
Smart Cities and Technologies
Publication details
- Journal / source
- Local Environment
- Volume
- 31
- Issue
- 8
- Pages
- 1235–1254
- Publication date
- 7 March 2026
- Language
- EN