Indigenous Peoples Day was announced in 1996 by the then Governor General of Canada. However, Indigenous communities have celebrated their culture and heritage at this time of year for generations.
On June 21st, Canada recognizes the history, heritage, resilience, and diversity of First Nations, Inuit, and Métis, the three groups of Indigenous peoples of Canada.
We’ve taken the opportunity to recognize National Indigenous Peoples Day and dive into how AI might be supporting Indigenous communities and protecting Indigenous lands and knowledge systems around the globe.
Protecting Land and Resources and Fighting Climate Change
The UN’s Permanent Forum on Indigenous Issues declares, “Protect Indigenous people’s land rights, and the whole world will benefit,” with UN speakers at one annual meeting explaining:
“Protecting the land and resource rights of indigenous peoples will not only provide security for historically exploited groups but also help the global fights against climate change and biodiversity loss.”
The symbiotic relationship between Indigenous peoples and the land and their deep knowledge and vital role as stewards is becoming recognized. Paul Robitaille, senior advisor of Indigenous relations for the Sustainable Forestry Initiative, says:
“Their languages, cultures, laws, governance structures, ways of knowing and being—they are all born from their place in the world. And in many regions, those places are forests. So Indigenous peoples are often a reflection of the forest and the land, and you really can’t separate the two.”
Indigenous peoples are the protectors of approximately 80% of the earth’s remaining biodiversity. AI’s fast and efficient data collation, analysis, and modelling are also becoming critical in protecting ecosystems. It is increasingly combined with the knowledge of Indigenous communities for powerful approaches to protect biodiversity and mitigate climate change.
In Sanikiluaq, an Inuit community in Nunavut, Canada, PolArctic has built an AI model that uses traditional Indigenous knowledge, data, and remote sensing to find fish locations to develop essential local commercial inshore fishing and conserve fishing traditions. The initiative helps local industry but understanding local fish habitats enables the local community to protect these areas from problems like sewage disposal and shipping. Future project development could focus on adding factors like shellfish growth rates and nutrient requirements to the model to enable local fishing operations to adapt and grow sustainably.
Coral Gardeners is an Indigenous-led project in collaboration with Cornell University that uses a bioacoustic AI modeling of coral reefs in Mo’orea, French Polynesia to identify where reef restoration is required. The project cultivates heat-resistant corals and transplants them to damaged reefs.
Conserving and Revitalizing Indigenous Languages
In New Zealand, the language spoken by the country’s indigenous Māori people, te reo, was only spoken by one in four Māori by 1960 and by very few children. Non-profit media organization Te Haku was founded in 1990 and began using radio stations and broadcasting services to share Indigenous language and culture.
After years of innovation and progress with conventional technologies, Te Haku saw the potential of AI to bring together years of stored knowledge. It created Korero Maori, an app for collecting language recordings.
The project has been expanded to include recordings in Hawai’ian, Cook Islands Māori, and New Zealand English, and the data is being used to train AI models. Te Hiku Media is collaborating with data scientists to build tools that New Zealanders can use, like voice assistants, in their preferred language.
In Canada, the Abundant Intelligences project has received a grant of $22 million. Its team consists of 37 experts from eight universities and 12 Indigenous community-based organizations across Canada. The team seeks to expand the definition of intelligence by collaborating with Indigenous communities to integrate their knowledge into AI research and development. It hopes to ensure that AI is inclusive of Indigenous knowledge systems.
Balancing a “Western Lens” in AI Training
Abundant Intelligences co-lead Jason Edward Lewis is a professor of computation arts at Concordia University and the University Research Chair in Computational Media and the Indigenous Future Imaginary. He says:
“We don’t have an AI ethics problem, we have an AI epistemology problem,”
An example from Lewis is that the “normative Western approach” favored by AI research assumes the user is an individual who prioritizes their own well-being. He says this ignores fundamentals of intelligence like trust, care, and community.
“Collapsing intelligence down to a rational, goal-seeking, self-serving agent—in all of our cultures, that kind of person would be seen as selfish, foolish, not a good community member, and not intelligent.”
Karim Jerbi, associate research member at project partner and AI research institute Mila, adds that “colleagues from the field of AI,” are realizing that building AI that learns from more diverse perspectives and values will produce better, more robust AI.
Indigenous Data Sovereignty
UNESCO says of AI, “It is crucial to ensure that technological advancements respect the unique characteristics of indigenous data and the rights of indigenous peoples.”
It adds that AI can play a vital role in preserving and passing on cultural heritage, including languages and practices, medicine and agriculture, astronomy, and storytelling. AI can help record and share this knowledge, especially among younger generations. However, says UNESCO, there is potential for “misuse, misappropriation, and dissemination of information without cultural sensitivity.”
Data sovereignty for Indigenous peoples is critical. Communities are advocating to own, control, and govern their data and ensure it is used in a way that’s aligned with their values. There are discussions, concerns, initiatives, and guidelines including from UNESCO surrounding data privacy and IP and databases that store Indigenous peoples information.
Frequently Asked Questions
How can AI help Indigenous communities protect land and natural resources?
AI can support land stewardship when it is grounded in local knowledge instead of open-web guesses. In Sanikiluaq, Nunavut, PolArctic built an AI model that combines traditional Indigenous knowledge, data, and remote sensing to find fish locations, support local inshore fishing, and help protect habitats from sewage disposal and shipping. Coral Gardeners in Mo’orea, working with Cornell University, uses bioacoustic AI modeling to identify reefs that need restoration and guide transplantation of heat-resistant corals. For question-answering tools, retrieval-first systems are safer for this kind of work; the provided benchmark says CustomGPT.ai outperformed OpenAI on RAG accuracy.
Can AI help preserve Indigenous languages without replacing fluent speakers?
Yes. AI can make approved lessons, vocabulary, and archives easier to find on demand, including across 93+ languages, while fluent speakers and knowledge keepers remain responsible for meaning, correction, and cultural context. A good rule is to use AI for access and search, not to overrule community authority on language use or ceremonial knowledge.
What does Indigenous data sovereignty mean when using AI?
Indigenous data sovereignty in AI means your community decides what knowledge enters the system, who can access it, and what should never be uploaded at all. The provided materials say uploaded data is not used for model training, the platform is GDPR compliant, and security controls are SOC 2 Type 2 certified. In practice, that supports a permission-first setup: private collections stay restricted, public answers come from approved sources, and citation-based responses make it easier to check what the system used.
How do you stop AI from imposing a Western lens on Indigenous knowledge?
Start with the source set. If an AI system searches community-authored documents, oral-history transcripts, policies, and approved archives first, it is less likely to default to mainstream assumptions. A retrieval-first workflow can scale: VdW Bayern DigiSol’s compliance assistant was trained on 3,620 documents and 25 million tokens, handled 7,000 queries in six months, and reduced task time by 50-60%. For Indigenous knowledge projects, the same pattern works best when sensitive outputs are also reviewed by knowledge keepers before wider use.
Can small Indigenous organizations test AI ideas without a big technical team?
Yes. No-code pilots are realistic for small teams. Nitro! Bootcamp launched 60 AI chatbots in 90 minutes for 30+ minority-owned small businesses, with a 100% success rate. That suggests a community organization can start with one narrow use case, such as program FAQs, archive search, or language-learning resources, before investing in a larger rollout.
Where should Indigenous governments or community organizations start first with AI?
Start with high-volume, rules-based questions such as service FAQs, eligibility rules, application steps, deadlines, and public program information. Those use cases are easier to ground in approved documents and easier to audit with citations. Speed also matters for adoption: Bill French said, “They’ve officially cracked the sub-second barrier, a breakthrough that fundamentally changes the user experience from merely ‘interactive’ to ‘instantaneous’.”
Related Resources
These articles extend the conversation around AI, equity, stewardship, and community well-being.
- AI Sustainability Overview — Explores how AI can support environmental goals and sustainability efforts in ways that connect with community-centered innovation.
- AI Privacy Balance — Examines how organizations can use AI responsibly while protecting sensitive data, consent, and trust.
- AI And Mental Health — Looks at the growing role of AI in mental health support and the ethical considerations that come with it.
- AI And Social Justice — Discusses how AI shapes fairness, access, and representation across social justice issues.
- AI In Marine Research — Highlights how AI is being used to study ocean ecosystems and strengthen research tied to global conservation efforts.