Abundant intelligences: placing AI within Indigenous knowledge frameworks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current trajectory of artificial intelligence development suffers from fundamental epistemological shortcomings, resulting in the systematic operationalization of bias against non-white, non-male, and non-Western peoples. We argue that these failings are, in part, the result of certain Western rationalist epistemologies that exclude many ways of knowing about the world, and therefore they cannot provide a sufficient foundation on which to adequately, robustly, and humanely conceptualize intelligence. We present a new research agenda, Abundant Intelligences, an Indigenous-led, Indigenous-majority international, interdisciplinary research program that imagines anew how to conceptualize and design artificial intelligence (AI) based on Indigenous knowledge (IK) systems. Abundant Intelligences draws on the rich plurality of Indigenous knowledge systems, bringing together diverse sets of thought, culture, and protocol together. We show IK systems provide one way to rebuild AI’s epistemological foundations and transform these tools’ current role in reinforcing colonial practices of exclusion, extraction, manipulation, and eradication into engines of abundance that enable us to care better for ourselves, our communities, and our world. Our proposition is to fully engage with AI to explore how different conceptions of intelligence could be embodied in these technologies. In this paper, we present the tenets of the research program in detail, account for our methodological approach, describe the impact and limitations, and conclude on a discussion of the implications of the program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it