Value Pluralism in the AI Ethics Debate – Different Actors, Different Priorities
Bibliographic record
Abstract
In the current debate on the ethics of Artificial Intelligence (AI) much attention has been paid to find some “common ground” in the numerous AI ethics guidelines. The divergences, however, are equally important as they shed light on the conflicts and controversies that require further debate. This paper analyses the AI ethics landscape with a focus on divergences across actor types (public, expert, and private actors). It finds that the differences in actors’ priorities for ethical principles influence the overall outcome of the debate. It shows that determining “minimum requirements” or “primary principles” on the basis of frequency excludes many principles that are subject to controversy, but might still be ethically relevant. The results are discussed in the light of value pluralism, suggesting that the plurality of sets of principles must be acknowledged and can be used to further the debate.
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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 | 0.083 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.014 |
| 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".