You can listen to the interview here:

We spoke about a question that sits at the heart of our research: how could artificial intelligence affect Indigenous Peoples, and what needs to be considered at the intersection of Indigenous Knowledges and AI?

It’s a conversation with two sides. AI carries real risks for Indigenous communities — of becoming another extractive force that takes Indigenous Knowledges without consent, credit or return, and of entrenching historical biases into systems that make decisions about people’s lives. But it also holds genuine promise: as a tool for preserving language and Country, and for building solutions to challenges that Indigenous communities themselves have identified.

Getting the balance right means Indigenous Peoples must be in the room — not consulted after the fact, but leading the design. Nothing about us, without us.

Speaking about this work on the radio, with Brendan there to speak to it from lived experience and cultural authority, was a reminder of why we do research in partnership in the first place. If you’d like to go deeper, our recent piece in Pursuit covers the research behind the conversation: AI must be built with Indigenous Knowledges, not against them.

I ended the chat with a line I’ll happily stand by: as AI speeds through our worlds, let’s put a human in the driving seat.

As Australia marks 50 years of NAIDOC Week, honouring the world’s oldest living culture, humanity’s newest technology is yet to reckon with a simple principle: “nothing about us, without us”.

The concern is that artificial intelligence (AI), like so many technologies before it, will become another extractive force that ‘takes’ Indigenous Knowledges without consent, credit or return.

The tension behind that question has reached the global stage. In his first encyclical, the Magnifica Humanitas, Pope Leo XIV cast AI as one of the defining moral tests of our age.

He talked about the dangers of AI becoming a tool for oppression, emphasising its potential to discriminate against minority groups, marginalised populations and Indigenous peoples.

He ultimately said we need to confront fundamental questions about the ownership, governance, accountability and distribution of the spoils of AI.

Our research found a dual reality: while AI is empowering, it also carries significant risks of entrenching existing and historical biases.

Preserving language and Country

The aim of our research, based on a First Nations-led research partnership that predates the industry’s now-visible reach, was to explore the intersection of Indigenous Knowledge Systems and AI.

We also wanted to weigh this against the risks and opportunities for Indigenous peoples around the world.

Our review of 53 academic studies published between 2012 and 2023 identified four ways AI can impact Indigenous Knowledge holders and communities.

We found AI can entrench existing biases, accelerating harm rather than preventing it.

So, protecting communities requires a new, engaged and consensual approach to training Large Language Models (LLMs). Indigenous Knowledge must be ethically embedded, properly recognised and genuinely valued as part of this process.

But there are also positives. AI can be a tool to preserve knowledge for community benefit and to design solutions that address Indigenous-identified challenges.

In fact, researchers around the world are already using AI to revitalise endangered languages, map culturally significant land and document oral traditions.

In Western Australia, Curtin University has developed a prototype app that identifies wildflowers in the area then plays audio of their names in the Noongar language, helping with pronunciation.

In California, researchers are partnering with the Amah Mutsun Tribal Band, a sovereign Indigenous nation of over 20 historic clans. Together, they are using machine learning to map plant distributions critical to the tribe’s cultural revitalisation.

Computational tools like this can support Indigenous Knowledge holders to document, preserve and keep their cultural heritage alive.

AI for Indigenous-led solutions

AI is also helping address challenges prioritised by Indigenous communities themselves.

In regional and remote Western Australia, Aboriginal medical clinics are trialling AI-assisted screening to combat diabetic retinopathy, a leading cause of preventable blindness.

It’s an initiative that directly addresses a critical gap in areas where screening rates have historically lagged.

Other Australian healthcare providers are using AI tools to improve ear health by detecting middle-ear diseases in Aboriginal and Torres Strait Islander children.

Beyond Australia, in Taiwan, healthcare programs are using predictive modelling to assess fall risks for Indigenous elders – bridging critical gaps caused by distance, skills shortages and high costs.

But these examples are not the whole picture.

Data, bias and damage

The same systems that support can also disrespect and damage Indigenous peoples and their Knowledge systems.

A child protection AI used in Aotearoa New Zealand disproportionately targeted Indigenous families. Research found that the algorithm referred Māori children to protection services at rates far higher than actual case numbers justified.

In Australia, an AI eye-screening tool misdiagnosed Aboriginal patients with high rates of false positives. Because the system was trained exclusively on datasets from Chinese clinics, it’s likely the algorithms failed to account for natural variations in retinal pigmentation.

These are not glitches. They are the predictable consequence of building systems on generalised data that doesn’t reflect the needs of specific populations.

But a deeper concern underlies these examples. Open data harvesting is central to how AI developers build technology.

For Indigenous peoples, who have been the subject of extractive research for centuries, this approach violates the principles of Free, Prior and Informed Consent.

It directly denies communities the right to govern their own information.

Two Indigenous-led global frameworks are explicit on this.

The CARE principles (Collective Benefit, Authority to Control, Responsibility and Ethics) and the OCAP principles (Ownership, Control, Access and Possession) confirms that communities are the decision-makers when it comes to their own data.

Most AI systems in use today were not designed with these principles in mind.

Centring Indigenous Knowledge to address AI bias

Critics have long warned that AI can reinforce existing structures of power. That Eurocentric ideologies shape AI, privileging individualism and Western ways of seeing the world, and the systems then reproduce those assumptions in the categories they learn.

Many researchers say AI must be examined through a decolonial lens, one that reveals the historical and ongoing operations of colonising power.

Indigenous Knowledge Systems offer a different starting point.

In Aotearoa New Zealand, Mātauranga Māori centres the relationships between humans, non-humans and te taiao (nature and all living beings). In sub-Saharan Africa, the Ubuntu philosophy centres on collective wellbeing.

These are not flavours to add at the end of an AI design process. They are alternative foundations on which new AI could be designed.

From principles to regulation

The work ahead is one of regulation, not goodwill.

Regulators must move principles like CARE and OCAP from aspiration into law, making Indigenous Data Sovereignty a binding standard that every AI system using Indigenous data must meet.

Australia’s voluntary AI Ethics Framework is one place this work can begin.

The technology industry must accept that co-design with communities is a precondition for deployment, not an afterthought. And companies building systems for health, child protection and other high-stakes settings must train their models only on data Indigenous peoples deem valid.

This involves centring and valuing Indigenous Knowledges in the data training process both through recognition, respect and financial recompense.

This goes to a larger set of questions that need clarification.

Who owns the training data behind the AI we use? Who benefits when it works? Who pays the cost when an algorithm gets it wrong?

As AI booms, the patterns we found have only sharpened.

We are building AI now and locking in its assumptions and biases. The only question is whether it reflects one way of seeing the world, or many.

This article was first published 3 July 2026 on Pursuit. Read the original article.

This was something I had never done before, and it was a great honour. One of the things I love most about my work is using science to directly influence policy — and it is rare and deeply rewarding to see research reach all the way into a parliamentary committee, where it can help shape decisions that affect people’s lives.

On our behalf, Professor Janeen Baxter, Associate Professor Sangeetha Chandrashekeran, and Professor Cameron Parsell drew on evidence-based research to speak on a range of issues, including the polarisation of wealth, renting, insecure housing, and the impacts of negative life course events. Our research extended beyond the page, helping inform discussion and shape thinking on deep and persistent disadvantage in Australia.

In August 2024, I spoke at the DoIT Summit, held during a period of considerable political change in the country. It was a different audience and a different venue than I had ever pictured for myself as an academic — but it was a genuine pleasure to contribute to the conversation through a research lens.

I was invited by Eranga Weeraratne, who has since become the Deputy Minister of Digital Economy, and who at the time was a driving force behind the ICT initiatives within the movement he was part of. I am grateful to him for the opportunity to bring an academic perspective into a wider discussion about the country’s future.

My talk was about how computational modelling can help governments make more tested, evidence-based decisions — decisions that aim for better outcomes for the public. Rather than relying on intuition or ideology alone, models let us simulate policies before they are rolled out, weigh trade-offs, and better understand who a decision helps and who it might affect. Used carefully, this is a tool for more transparent, science-informed policy design.

I delivered the speech in Sinhala, to an audience deeply invested in where the country was heading. I spoke with hope — hope that whoever formed the next government would embrace science-backed, carefully designed policy as a foundation for governance.

The speech is in Sinhala.

Looking back now, I am glad to have taken part in that conversation. Whatever one’s politics, the aspiration I spoke to — that public decisions should be tested, evidence-based and designed with care — is one I hope every government works towards.