20 posts tagged with "Data sovereignty"

The public discussion around artificial intelligence often treats American and Chinese models as if they come from completely different technological worlds. Politically and legally, they do operate under different jurisdictions. Their companies have different ownership structures, regulatory obligations, cloud environments and terms of service. Those differences matter.

But when we look at how the models themselves are trained, the broad pattern is strikingly similar. OpenAI, Anthropic, Google, Meta, Alibaba and DeepSeek all rely on some mixture of large public datasets, licensed or partner material, human-created examples, synthetic data and extensive post-training. The details vary, but the core industrial process is recognisable across borders.

For Māori organisations, this means “American model” versus “Chinese model” is not a sufficient risk framework. The better questions are what data sources were used, how transparent the developer is, what product is being used now, where current prompts are processed, what jurisdiction applies, and whether the model can be run locally without sending new Māori information back to the developer.

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?

A few years ago the AI part of a GIS workflow was simple enough. Open ChatGPT, type a question, see what comes back. Sometimes it was useful. Sometimes it confidently invented a Python function. At least the administrative overhead was low.

That has changed. A Māori GIS practitioner wanting help with an ordinary piece of work can now face a small set of decisions before the work starts. ChatGPT, Claude, Gemini, Copilot or something more specialised? A fast model or the one that thinks for longer? Ordinary chat, a project, a research mode, a coding agent or a persistent workspace? Is the task worth using a limited premium allowance? Does the system need three source documents or the whole project folder? Can it use the web? Can it run code? Will it remember anything later? Most importantly for Māori GIS, what information are we about to give it, and should that information leave the environment it is already in?

This is starting to look like a professional skill in its own right. Knowing AI no longer means being good at typing questions into one chatbot. It increasingly means knowing how to select and manage several different kinds of machine assistance, give them the right amount of context, use expensive capability where it matters, keep data under appropriate control, and check the result before it quietly becomes part of the GIS.

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.

For a long time I thought the obvious measure of a GIS project was the thing it produced. A map was finished, a map book was printed, a database was delivered and a set of layers worked. Outputs are comforting because they can be counted, photographed and put into reports. Māori GIS work gradually taught me to ask a much less comfortable question: what happens after the mapper leaves?

That question changed almost everything. A map can be excellent and still leave the people who need it dependent on the person who made it. A GIS can be technically sophisticated and still become useless when the one person who understands the folders moves on. A training event can be inspiring and still achieve almost nothing if the participant returns home and cannot reproduce what they saw. The real test of capability is not whether the system works while the expert is standing beside it. It is whether the work can continue afterwards, under the authority of the people who need it and in a form that can be carried forward.

Over time I came to see that this was more than a sustainability problem in the usual project-management sense. In Māori settings there was often an intergenerational dimension to the work. Whenua, place names, histories and community information were not temporary project assets. People might be using them for a Treaty claim today, environmental planning tomorrow and something quite different in ten or twenty years. The question was therefore not only whether the system survived the funding period, but whether knowledge, context and authority could survive changes in staff, software and organisations.

I am not Māori and do not claim a Māori worldview as my own. What I can describe is what working alongside Māori practitioners and communities taught me about capability. The strongest projects were not simply those that delivered technology. They were the ones that increased rangatiratanga over the work, reduced avoidable dependence and left people better able to make their own decisions about their information.

GIS has a habit of making uncertainty look tidy. Put an old plan on a screen, add a modern cadastral layer, draw a line and label it, and suddenly the result looks as though somebody has settled the matter. The line is crisp, the coordinates are precise, the parcel beneath it is authoritative and the aerial image may be sharp enough to see fences and buildings. Everything about the screen tells the viewer that the answer has been found.

A great deal of Māori claimant mapping taught me the opposite. Sometimes the most accurate result is to admit that the answer is not known. Historical land blocks do not always line up cleanly with modern parcels. Rivers move. Surveyors make assumptions. Names change. Plans disagree. Natural features disappear. Areas recorded on paper do not always agree with areas calculated from geometry. Some source plans are missing altogether. The software is always willing to draw a line, but that does not mean the evidence is willing to support one.

In one of the Northland mapping methodologies we had a very useful description for this: State of Unknown. It was not grand terminology. It simply meant that the available information did not justify pretending there was one certain answer. Over time I came to think of it as one of the most useful professional disciplines GIS can offer, particularly in Māori GIS, where whenua is not merely a parcel to be measured and knowledge of place does not become more authoritative simply because it has been converted into coordinates.

I am not Māori and I do not claim to speak from within te ao Māori. What I can describe is what repeated work alongside Māori claimants, practitioners, researchers and communities taught me about the limits of a purely technical way of looking at land and information. GIS tends to begin with objects: points, lines, polygons, parcels and layers. Much of the Māori mapping work I encountered began somewhere more relational, with people, whenua, whakapapa, history, responsibility and the authority to say what a place means. The map could help represent parts of those relationships, but it could never become the relationship itself.

One of the first things GIS teaches you is confidence. A line appears on the screen cleanly, precisely and apparently without argument. You can zoom into it, measure it, colour it, calculate its area and print it at A0. Once information has travelled through a computer, it acquires a peculiar authority. It looks as though somebody, somewhere, must know exactly where it is and exactly what it means. One of the first things Māori claimant mapping taught me was almost the opposite. Historical land blocks did not always line up neatly with the modern cadastre. Old plans could disagree with one another. Rivers moved. Survey descriptions depended on natural features that had changed or disappeared. Names varied between sources. Areas written on historical records did not always agree with areas calculated by modern GIS. Sometimes a plan was missing altogether, and sometimes two apparently authoritative documents contradicted one another.

In one of the Northland mapping methodologies we had a wonderfully unfashionable description for this: State of Unknown. Rather than forcing the available information to produce an answer simply because the software was capable of drawing one, we recorded that the answer remained unresolved. At the time it was a practical classification for awkward historical geography. Over the next twenty years it became something closer to a professional philosophy. The temptation in GIS is always to resolve, align, categorise and complete. State of Unknown was a reminder that sometimes the more professional thing is to preserve the uncertainty.

What changed more slowly for me was the way I thought about the thing underneath the map. In conventional GIS the land can easily become a surface on which information is located. Parcels sit on it, roads cross it, rivers run through it and points are placed where events occurred. Working alongside Māori claimants, practitioners, researchers, iwi and hapū repeatedly challenged that habit. Whenua was not simply the backdrop to the information. It sat within relationships, whakapapa, occupation, memory, responsibility, history and identity. The map could help represent parts of those relationships, but it could not contain them all.

I am not Māori, and I do not claim to explain te ao Māori or speak for the people whose knowledge shaped the projects I worked on. My part in this history has been much more practical. I have built maps, recovered old plans, configured GIS systems, organised workshops, trained people, supported conferences, found data, developed resources, fixed broken things, produced programmes, dealt with government agencies and occasionally discovered that an ambitious twenty-first-century mapping project was being held together by a cardboard box of DVDs and somebody remembering where the extension lead had gone. What changed my practice was being in rooms where somebody else understood the place, history or meaning far better than I did and learning that the technical role was most useful when it supported that authority rather than competing with it.

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.