Repertories of evaluation in AI ethics: Plurality in professional responsibility and accountability
Bibliographic record
Abstract
We examine the ways AI developers engaged in ethical discourse. AI ethics are defined as values, principles, and techniques that guide moral conduct in developing and deploying AI technologies. Comparing the pluralities of claims about AI ethics among developers, we aim to respond: How do AI developers evaluate their responsibility regarding the social implications of AI technologies? Based on qualitative analysis, this paper argues that in the construction of professional responsibility and accountability, developers of AI attend to different sets of epistemic and normative concerns organized around recursive forms of judgment named repertoires of evaluation. We identify four essential repertoires anchored on notions of technical efficiency criteria. However, they mobilize these criteria differently when combined with other values. This study connects the high-level guidelines around AI ethics with qualitative descriptions of developers’ values and experiences and proposes a theoretical framework to inspect them.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".