Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the recent wave of progress in artificial intelligence (AI) has come a\ngrowing awareness of the large-scale impacts of AI systems, and recognition\nthat existing regulations and norms in industry and academia are insufficient\nto ensure responsible AI development. In order for AI developers to earn trust\nfrom system users, customers, civil society, governments, and other\nstakeholders that they are building AI responsibly, they will need to make\nverifiable claims to which they can be held accountable. Those outside of a\ngiven organization also need effective means of scrutinizing such claims. This\nreport suggests various steps that different stakeholders can take to improve\nthe verifiability of claims made about AI systems and their associated\ndevelopment processes, with a focus on providing evidence about the safety,\nsecurity, fairness, and privacy protection of AI systems. We analyze ten\nmechanisms for this purpose--spanning institutions, software, and hardware--and\nmake recommendations aimed at implementing, exploring, or improving those\nmechanisms.\n
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it