Coproduction, Ethics and Artificial Intelligence: A Perspective from Cultural Anthropology
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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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.127 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".