Coproduction, Ethics and Artificial Intelligence: A Perspective from Cultural Anthropology
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
Over the past five years, artificial intelligence (AI) has been endorsed as the technical underpinning of innovation. Sensationalist representations of AI have also been accompanied by assumptions of technological determinism that distract from the ordinary, sometimes unassuming consequences of interaction with its systems and processes. Drawing on scholarship from cultural anthropology, along with science and technology studies (STS), this paper examines coproduction in a Canadian AI research and development context. Through interview responses and field observations it presents sites of sociotechnical entanglement and ethical discussion to highlight potential spaces of mediation for anthropological practice. Emerging themes from the experiences of AI specialists include the negotiability of technology, an ethics of the everyday and critical collaboration. Together this returns to an initial approach into a situated understanding of artificial intelligence, negotiating with broad, sensationalist perspectives and the more commonplace, backgrounded cases of narrow research.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it