Last Updated on February 25, 2026
Artificial intelligence (AI) is quietly weaving itself into fabric of our daily lives, powering everything from health care decisions to law enforcement tools. It raises important questions around bias, surveillance, and the concentration of power in the hands of those who control these technologies. Unfortunately, mainstream discourse is being dominated by Western academia, corporate think tanks, and government institutions.
At the same time, most AI-generated content is considered to be exempt from intellectual property claims, effectively placing vast amounts of machine-produced data into the public domain. This legal shift raises even more questions about ownership, consent, and exploitation—particularly for the world’s 7,000 Indigenous Peoples and nations whose knowledge systems have long been extracted and used without permission.
As much as we want Artificial intelligence to be neutral, it is anything but.
The reality is predictive: artificial intelligence systems have no choice but to reflect the priorities and power structures of its architects. This means AI tech is replicating the logics of colonization while magnifying the structural erasure of Indigenous voices that are already heavily marginalized.
As Suzanne Kite (Oglála Lakȟóta) noted in a 2021 interview with MIT Press:
“We’re not invited to the table where these technologies are being created… And when we are, it’s usually as a checkbox—not as sovereign knowledge holders with something vital to offer.”
Thankfully, Indigenous academics, technologists, and communities are taking the initiative to speak out–without any prompting (pun intended). And by raising their own voices, they are highlighting the fact that indigenous knowledge systems offer a valuable counterpoint.
It isn’t just about making AI more “inclusive”. Rather, it’s about reshaping the very foundations that AI is built on.
As Dr. Angie Abdilla, a Palawa woman and founder of Old Ways, New, said in a 2020 paper co-authored with AI researcher Kate Crawford,
“Indigenous knowledge systems are not supplementary to Western paradigms; they are full epistemological frameworks that provide critical ethical and ecological insights”
Their call is not for token inclusion, but for a redefinition of what intelligence, ethics, and accountability mean in these modern times.
Mainstream data ethics begins with an assumption that data is a raw resource that can be mined and optimized. Indigenous knowledge systems challenge that assumption with their focus on interdependence, relationality, and long-term stewardship:
“Our stories are data,” said Dr. Jason Edward Lewis (Kanaka Maoli/Samoan), co-founder of the Indigenous Protocol and Artificial Intelligence (IPAI) Working Group. However, “they contain encoded knowledge about relationships, responsibilities, and balance. When you strip them of that context, you’re not just distorting the data—you’re violating it.”
This way of thinking changes how AI systems make decisions as well as the outcomes of those decisions.
For example, the Canadian government admitted in 2021 that British Columbia’s Ministry of Children and Family Development used predictive analytics to flag down families that were deemed at “risk”.
“This was a digital continuation of the colonial surveillance state,” said Mary Ellen Turpel-Lafond (Cree), former B.C. Representative for Children and Youth, in a panel discussion hosted by the First Nations Technology Council.
The program disproportionately affected Indigenous families and it was eventually discontinued because it operated without the knowledge or consent of the communities that were being targeted.
Not much has changed since then, with the United Nations Special Rapporteur on the Rights of Indigenous Peoples even warning that AI systems “frequently exacerbate existing inequalities” and “raise profound questions of consent and sovereignty.”
“Lakota ethics begin with acknowledging your limits,” writes Suzanne Kite (Oglála Lakȟóta), a researcher and artist whose work centers Lakota epistemologies in machine learning. “Western tech begins with assuming you have none.”
Kite emphasizes the concept of wówačhaŋtognaka, a Lakota value that emphasizes humility, listening, and moral responsibility—not only to other humans but to the more-than-human world. In Lakota thought, intelligence is not raw computational power; but an act of understanding and care.
“You don’t build AI systems that extract from the land and people without thinking about what you owe to them. You don’t assume everything should be automated,” Kite said in an interview with MIT Press.
We need to understand that these systems do not exist in a vacuum. They are built on data that is often extracted without consent—and maintained by institutions whose interests rarely align with Indigenous sovereignty or well-being.
This is not a minor aesthetic difference. It is a radically different model for how to build and govern technology.
In Aotearoa (New Zealand), Māori scholars are putting these principles into practice. The University of Auckland, in partnership with Māori data sovereignty organizations, is embedding *tikanga Māori* (Māori ethical frameworks) into the logic of its AI systems.
“We’re not trying to fit Māori knowledge into AI,” said Dr. Karaitiana Taiuru (Ngāi Tahu, Ngāti Toa, Ngāti Kahungunu), a Māori digital rights expert. “We’re designing AI that fits within Māori knowledge frameworks”.
Such projects emphasize communal responsibility, long-term consequences, and consent—values that mainstream AI often discards in pursuit of speed and scale.
These initiatives are part of a broader movement toward Indigenous data sovereignty and the right of Indigenous nations to control the collection, access, and use of data that involves them. Organizations like the First Nations Information Governance Centre (FNIGC) in Canada have been advocating for this since the 1990s, developing principles like OCAP® (Ownership, Control, Access, and Possession) that offer a roadmap for ethical data governance.
Indigenous scholars are clear: this is not about adding Indigenous voices to existing systems. It’s about rethinking the systems entirely.
“Inclusion often means assimilation,” said Dr. Lewis. “What we need is transformation.”
Transformation requires dismantling power structures that place corporate interests over community survival. It means investing in Indigenous-led AI labs, like the Indigenous Futures Research Centre at Concordia University. It also means asking harder questions like whether some technologies should even exist.
Indigenous scholars are clear: this is not about adding Indigenous voices to existing systems. It’s about rethinking the systems entirely.
It also requires journalists, policymakers, and developers to seek out alternate viewpoints and take the time to actually listen and understand them.
The fact is, Indigenous knowledge systems aren’t just “alternative” perspectives. They are tried-and-tested modes of understanding that have safeguarded ecological and social balance for millennia.
If artificial intelligence is going to serve a livable, just future, it will need the guidance of those who have already been doing this work—not for profit, but for generations.
Across the world right now, Indigenous peoples are facing a renewed assault on their lands, rights, and ways of life.
From extractive industries pushing deeper into ancestral territories, to governments rolling back hard-won protections,
the threat is global—and it is growing.
What makes this moment especially dangerous is how little attention it receives.
Stories like this are disappearing in real time. Mainstream media
barely covers it and the coverage is fragmented. The public is left without the context
needed to understand what is truly at stake.
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