What respect requires: Working with artificial intelligence and Indigenous Knowledges
Bronte Chiang
AI tools, including GenAI systems, are already being used in academic and research contexts where Indigenous Knowledges are present. This section does not assume that such use is neutral, inevitable, or universally appropriate. Instead, it recognizes that researchers, instructors, students, and institutions are already engaging with these tools and require guidance grounded in responsibility, respect, and accountability, rather than in efforts to make the technology work better (Lewis et al., 2020).
Responsibility and respect in relation to AI do not emerge from the technology itself, but from the choices people and institutions make about how, when, and whether these tools are used. In contexts involving Indigenous Knowledges, this means recognizing that AI systems cannot be relied upon to recognise cultural Protocols, relational responsibilities, or community authority on their own; responsibility must be anchored in governance, relationship, and accountability rather than in outputs or “better” prompts (Lewis et al., 2020).
Purpose, authority, and accountability in practice
Example: Report of the Artificial Intelligence, Data Sovereignty, and Cybersecurity Task Force produced by the Cherokee Nation
Example: Salmon Vision project with Haíɫzaqv (Heiltsuk) Nation
For scholarly and instructional work at the intersection of AI and Indigenous Knowledges, these examples underscore the necessity of beginning with questions of authority, purpose, and consequence rather than with tools. Questions such as:
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- Who defines the purpose of the work?
- What forms of consent and authority are present?
- What responsibilities accompany the knowledge being engaged?
- Who bears the consequences if it is misrepresented or reused out of context?
Indigenous‑centred approaches to artificial intelligence require attention to community‑specific Protocols and processes. Any guidelines that emerge functions as entry points into accountable relationships, rather than as universal principles or checklists (Lewis, 2020).
Together, these examples show that respectful engagement with AI is not about adopting best practices or assuming harms can be resolved through design. It involves Indigenous voices determining whether and how AI is used, grounding decisions in clearly defined purposes and accountability to community values. Respectful engagement also includes the ongoing possibility of refusal and non‑use, recognizing that responsibility may mean choosing not to involve AI at all.
