Towards abundant intelligences: Considerations for Indigenous perspectives in adopting artificial intelligence technology
Bibliographic record
Abstract
Artificial Intelligence (AI) applications in healthcare are evolving rapidly. The integration of AI into the Canadian healthcare system has demonstrated significant potential for enhancing the efficiency of care and improving patient outcomes. However, as this transformative technology continues to advance, it is crucial to take into account the unique perspectives and requirements of Indigenous Peoples in Canada. This article delves into the political, ethical, and practical considerations associated with introducing AI into Indigenous healthcare, emphasizing the paramount importance of equity and inclusion, which are rooted in the Two-Eyed AI framework. It also underscores the significance of co-creating AI technology in collaboration with Indigenous communities and multidisciplinary development teams. To illustrate these principles, this article spotlights an international AI epistemology-focused working group example. Healthcare professionals who engage with AI, whether it be through research, management, development, or leadership are implicated with this contemporary paradigm shift in decolonizing novel AI technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.034 | 0.110 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".