Bibliographic record
Abstract
We undertake an expansive examination of the terms Human-in-the-loop, Human-on-the-loop, Human-out-of- the-loop, and Human-in-command, as used recently in AI development, relative to their ethical implications and implicit assumptions. Tracing the history and development of the ‘Human ...’ terms, we explore the contexts and uses present from their beginnings. We follow with a discussion of the ethical outlook which the origins of the terms and their recent rebranding for AI development under the notion of oversight have engendered. Drawing upon certain insights of Bruno Latour for support, we suggest that Latour’s ‘forgotten ethical intermediaries’, folded into our technologies, have their analogue in the view of the human as a component of automated systems alternating with a role of human oversight. We argue that a more ethical human relation to technology can be recovered through an expansive emphasis on human participation in technology producing communities. Finally, we present a flexible new scale, the IGP scale, to rate such participation.
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How this classification was reachedexpand
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".