A Double-Edged Sword at the UN Permanent Forum
At the United Nations Permanent Forum on Indigenous Issues, experts described artificial intelligence as both an ally and a threat for Indigenous peoples. AI is increasingly woven into conservation and AI climate adaptation projects, from fire detection to land-use planning. Yet a study led by former forum chair Hindou Oumarou Ibrahim underlined that the AI environmental impact can be severe. Training and running large models demands huge amounts of energy, water and critical minerals, driving land-grabbing, water overexploitation and land degradation. Speakers stressed that Indigenous communities have safeguarded some of the world’s most intact ecosystems without satellites or algorithms, and that any deployment of AI must respect their rights, worldviews and aspirations. For many leaders, the central question is not whether AI is used, but on whose terms, and with what safeguards for Indigenous land rights and self-determination.
How AI Conservation Tools Help Defend Indigenous Territories
Despite the risks, AI conservation tools are already helping Indigenous communities protect their territories. Using satellite imagery, sensors and machine learning, these systems can detect deforestation, illegal mining, wildfires and water contamination far faster than manual patrols. In the Katukina/Kaxinawá Indigenous Reserve in Acre state, an artificial intelligence tool developed by a nonprofit and a technology company ranks the area among the top five for deforestation risk. Indigenous agroforestry agents use this information to spot invasions, illegal logging, hunting and nearby fires, increasing safety and supporting Indigenous land rights enforcement. In other regions, Indigenous pastoralists combine participatory mapping with predictive AI to anticipate severe droughts and secure migration corridors for livestock. Inuit communities are blending traditional knowledge with AI models and time-series analyses to locate new fishing grounds as climate change shifts species. When guided by Indigenous knowledge, AI climate adaptation and monitoring can strengthen resilience rather than replace ancestral stewardship.
The Hidden Environmental Costs: Energy, Water and Minerals
Behind every AI system tracking forest loss or wildlife movements, there are data centers and server farms with significant environmental footprints. These facilities require constant electricity for computation and cooling, and intensive data center water use to keep hardware from overheating, deepening the overall AI environmental impact. To build and power them, companies rely on critical minerals, large land parcels and energy infrastructure, all of which can intensify pressure on Indigenous territories and surrounding ecosystems. Ibrahim’s study warned that the full AI lifecycle risks land-grabbing, water exploitation and land degradation, even when AI is marketed as a green solution. Expanding data center networks, energy projects and mining concessions can encroach on lands that Indigenous peoples have historically stewarded, undermining the very ecosystems AI tools claim to protect. For communities on the front lines, AI for conservation is inseparable from the material footprint of the servers that make it possible.
Data Governance, Consent and the Risk of New Surveillance
The debate is not only about servers on Indigenous land, but also about who controls the data flowing through AI systems. Sensors, drones and satellites collect detailed information about forests, water sources, wildlife and human movements. Without clear rules around data governance and consent, these AI conservation tools can easily become instruments of surveillance, enabling governments or companies to monitor Indigenous territories without community permission. That risks reproducing colonial patterns of control, where outside actors extract knowledge and make decisions while local people are sidelined. Experts at the forum emphasized the need to align AI projects with Indigenous rights, including free, prior and informed consent. Data gathered on Indigenous lands should be co-governed, with communities deciding what is collected, how it is used and who benefits. Otherwise, AI meant to protect land could instead be used to police it, or to justify new forms of enclosure and resource extraction.
Toward Community-Led, Rights-Respecting AI Systems
Across different regions, Indigenous technologists and leaders are outlining how AI could genuinely support Indigenous land rights and environmental sustainability. The Sámi AI Lab, for instance, explores how AI can be designed around Sámi views and norms, rather than imposed from outside. Its work shows how AI can democratize access to advanced modeling, giving communities tools to challenge powerful institutions using their own evidence and frameworks. Ibrahim and other experts argue that AI should be used in culturally appropriate ways, with Indigenous peoples setting priorities and retaining control over both land and data. Proposals emerging from the forum include community-led governance of AI projects, strict protections against land-grabbing for data centers and energy infrastructure, and meaningful partnership between traditional knowledge holders and technologists. In this vision, AI for the forest and AI for the server farm are reconciled only when communities most affected hold real power over how the technology is built and deployed.