Trust Issues: Discrepancies in Trustworthy AI Keywords Use in Policy and Research
Bibliographic record
Abstract
How governments, practitioners, and researchers define artificial intelligence (AI) ethics significantly impacts the AI models and systems designed and deployed. Thus, the convergence of policy goals and technical approaches is necessary for international norms and standards on trustworthy AI. Defining, much less achieving trustworthy AI characteristics, however, entails clear communication through consensus on the meaning of field-specific terms. This paper presents an analysis of over 322,000 scientific research papers and the national documents from five countries (Australia, Canada, Japan, the United Kingdom, and the United States) on trustworthy AI in order to provide an in-depth review and comprehensive understanding of the similarities and differences between governments’ and researchers’ definitions and frameworks. While we identified substantive and relevant differences among policy documents and scientific research, the differences do not represent substantial disagreements among the common principles for trustworthy AI terms. Overall we found broad agreement across documents’ trustworthy AI term use, suggesting that nuanced differences could be overcome in an effort to create more global policies and aligned research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.382 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.012 | 0.065 |
| Scholarly communication | 0.032 | 0.041 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.009 | 0.010 |
| 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".