One post tagged with "mapping"

The previous articles in this series establish several uncomfortable but useful facts. Public Māori material has already entered web-scale AI training corpora. Māori-related information is likely far more extensive than the visible te reo Māori subset because large quantities of Māori history, planning, legal and environmental material are written in English. At the same time, models can remain weak in Māori language and context because the global training corpus is overwhelmingly larger than the Māori material within it.

We also know that entering a document into ChatGPT, Claude, Gemini, Copilot, DeepSeek or Qwen today is a separate issue from historic foundation-model training. Current prompts may be used only for inference, may be retained for a period, may be excluded from model training under enterprise terms, or may be eligible for later model improvement depending on the product and settings. Local AI can change the data path again by keeping current documents and inference on infrastructure controlled by the organisation.

For Māori GIS, this is not an abstract debate. Mapping is where information becomes spatially explicit. AI can make hidden relationships easier to discover, and GIS can then turn those findings into points, lines, polygons and maps that appear much more certain than the underlying evidence.

The practical challenge is therefore not to decide whether AI is good or bad for Māori mapping. It is to decide which parts of the AI architecture should be used for which kaupapa, with what information, and under whose authority.