7 posts tagged with "Privacy"

Local AI deserves far more attention in Māori technology discussions. It also deserves more careful discussion. Running a model locally can solve a very specific and important problem: information does not necessarily have to be transmitted to an external AI provider for processing. That is a substantial advantage, but it is not the same as solving Māori data sovereignty.

The distinction matters because Māori data sovereignty is not simply about geography. Keeping information in Aotearoa, on an iwi server or even on a disconnected laptop can strengthen control, but the deeper question remains who has authority over the information, who may use it, for what purpose, and with what consequences.

One of the least useful questions we can ask about a new technology is simply, “Is AI safe?” AI is not one system. A public chatbot, Microsoft Copilot, a commercial AI API, a local language model, a local document-search system and an entirely Māori-controlled AI environment may all contain a large language model somewhere in the architecture, but beyond that they can be profoundly different.

If Māori data sovereignty is fundamentally concerned with authority and control, those differences matter. The simplest way to understand them is to stop looking at the AI brand and follow the information. Where does it start, where does it travel, who processes it, what is retained, and who can change the rules later?

A common warning about generative AI says that anything typed into a chatbot becomes part of the model. That is too crude to be useful. What actually happens depends on the provider, the product, whether the account is consumer or enterprise, the settings in force, whether feedback is submitted and whether the organisation has negotiated different retention terms.

For Māori organisations and GIS teams, this matters because the difference between a public chatbot and an enterprise or local system can be substantial. A planning report, shapefile attribute table, field note, Treaty research document or set of candidate place names may be handled very differently depending on the service used.

The first technical point is simple: entering a prompt normally causes inference. The already-trained model processes the current context and produces a response. The model is not usually changing its weights while the user types. After that inference, however, the service may retain the interaction, and in some consumer products the retained material may be eligible for later model improvement. Those are separate stages.

This article looks at current published policies as at 4 September 2026. They change, so the exact product terms should always be checked before important Māori or organisational data are used.

Local AI has become much more useful in 2026. A normal desktop computer can now run a capable language model, search a collection of PDFs, extract structured information, help write Python or QGIS expressions and keep the working material on the machine. That makes it particularly interesting for Māori GIS, where the question is often not only whether AI can do the work, but where the information goes while it is doing it.

The most useful local workflow is not a private version of ChatGPT that somehow knows everything about Māori GIS. It is more modest and, for research, more useful. Give the system documents you are allowed to work with. Ask it to find candidate information. Keep the original wording and source reference. Have a person review the result. Check any geographic match against an appropriate source. Only then let the reviewed information become GIS data.

That distinction matters. A language model is quite capable of reading a reference to an old kāinga, deciding what modern place it probably means and supplying coordinates that look perfectly respectable. A GIS will then plot those coordinates without showing the slightest concern about whether they were invented. Local AI changes where the mistake was made. It does not make the mistake authoritative.

The practical question is therefore: what can we now run locally, what is genuinely offline, what appears promising for te reo Māori, and where should AI stop before the map begins?

GIS people are naturally rewarded for adding information. Another layer appears, another dataset is joined, another set of points is mapped and the system becomes richer. That is usually presented as progress. Māori GIS taught me that a good map is sometimes defined just as much by what it deliberately leaves out.

That is not the same as hiding an error or producing an incomplete map through carelessness. It is a decision about kaupapa, authority and responsibility. Some information is too sensitive for a public map. Some locations should be generalised. Some details belong in an internal system rather than a website. Some knowledge may not need to become digital at all. The fact that GIS can map something does not mean it should.

One sentence eventually became one of the clearest principles in Ngā Poutama Matawhenua: we will never ask you for your data. It sounds like a small operational rule for a training programme. In reality it contains much of what twenty years of Māori GIS work taught me about authority, trust, rangatiratanga and the limits of technical access.

GIS people are trained to acquire data. We search for layers, download services, request files, convert formats and build databases. The professional instinct is often that more information is better. In many ordinary projects that instinct is useful. In Māori GIS it can become dangerous if the technical desire to collect information gets ahead of the questions that should come first: whose information is this, what relationships does it sit within, who has authority over it, why is it being collected and what responsibilities come with holding it?

I am not Māori and do not claim to define tikanga for other people's information. What I can describe is the change in my own practice after years of working with claimant information, iwi and hapū projects, Māori GIS practitioners and training programmes. The more I learnt, the less comfortable I became with the ordinary technology assumption that the person who possesses the file is therefore entitled to organise, copy, analyse and publish it. Technical custody and legitimate authority are not the same thing.

That distinction sits close to rangatiratanga. In a GIS context, I have come to think about it practically: who can decide what is collected, who can see it, where it lives, what leaves the organisation and whether a map should be made public at all? A platform can give people access while quietly shifting those decisions elsewhere. A community can be invited into a modern digital system while losing control over where the information goes. The interface may look empowering while the architecture does the opposite.

The May 2026 Ngā Poutama Matawhenua wānanga, AI Haumaru, AI Motuhake mō te Māori GIS kaupapa, focused on practical uses of artificial intelligence while protecting Māori GIS data and maintaining control over sensitive information.

The recording and related material are collected on the Ngā Poutama Matawhenua resources page, where the current links can be maintained in one place.

The session was held on Friday 22 May 2026 as an extended online wānanga. The framing matters. AI haumaru is not only a cyber-security question, and AI motuhake is not simply a technical setting. For Māori GIS, the deeper issue is whether people retain practical authority over what information is supplied, what the system is allowed to do with it, how outputs are checked and whether the technology strengthens or weakens rangatiratanga over the mahi.