Artificial intelligence and Indigenous Knowledges
Rachel Chong
Artificial Intelligence (AI) is a tool with many variations. AI tools can be general or they can be created for specific specialized tasks. General AI tools include products like ChatGPT, Gemini, Copilot, and more. Within AI tools there are also free and paid versions of the tool, with varying levels of output. The AI landscape is constantly evolving and changing. With that in mind, this chapter includes some general concepts to consider when engaging in AI use in relation to Indigenous Knowledges.
AI is a computer-generated program that requires training with data to produce results. “AI outputs depend heavily on the training data, making it crucial for users to recognize the limitations and potential biases in AI results; for example, biased input data will naturally produce biased outcomes” (Kourotakis, 2024). The AI results will vary based on the data input and the search algorithms (Kourotakis, 2024). For most general AI products, they are being trained on large data sets widely available on the internet. The internet datasets are embedded with the bias of their creators (Lewis, Whaanga, & Yolgormez, 2025). When you consider who is able to access the internet, who has the technical skills to be involved in creating web content, who has access to basic technology, there is a gap and some people are excluded (Kourotakis, 2024). Indigenous Peoples are often underrepresented in these digital spaces. As a result of this gap, general AI platforms are primarily receiving data training about Indigenous Peoples not from Indigenous Peoples themselves, but from settler perspectives about Indigenous Peoples. This creates an inherent bias in the data set and within the general AI tools (Lewis, Whaanga, Yolgormez, 2025).
In addition, AI tools rely on a tremendous number of natural resources, from power, to cooling – consuming vast amounts of energy and water. When using AI, it is important to consider the environmental cost and how AI is actively contributing to the destruction of Indigenous lands via hydro dams for power, mining for chip development, or water diversion to cool AI data centers. Each AI use, comes with a very real cost to Indigenous communities lands (Hao, 2025).
