Mapping the <scp>ethic‐theoretical</scp> foundations of artificial intelligence research
Bibliographic record
Abstract
Abstract The issue of artificial intelligence (AI) ethics is a prominent research subject. While there is a compendious literature that explores this area, surprisingly little of it makes explicit reference to the ethic‐theoretical foundations upon which it is built. To address this matter, this study makes an examination of the AI ethics literature to identify its ethic‐theoretical foundations. The study identifies the lack of AI ethics literature that draws upon seminal ethics works and the ensuing disconnectedness among the publications on this subject. It also uncovers numerous non‐Western ethic‐theoretical positions that can be adopted and may afford new insight into AI ethics research and practice. Employing these alternative lenses may obviate the tendency for Western worldviews to dominate the academic literature. The study provides some guidance for future AI ethics research which should endeavor to clearly articulate its chosen ethic‐theoretical position, and for practice which could benefit from understanding and articulating the principles upon which AI systems are founded. It also provides some observations of, and guidance for, the utilization of Litmaps software in the conduct of Literature reviews.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".