Bibliographic record
Abstract
Recent developments in large language models and computer automated systems more generally (colloquially called ‘artificial intelligence’) have given rise to concerns about potential social risks of AI. Of the numerous industry-driven principles put forth over the past decade to address these concerns, the Future of Life Institute’s Asilomar AI principles are particularly noteworthy given the large number of wealthy and powerful signatories. This paper highlights the need for critical examination of the Asilomar AI Principles. The Asilomar model, first developed for biotechnology, is frequently cited as a successful policy approach for promoting expert consensus and containing public controversy. Situating Asilomar AI principles in the context of a broader history of Asilomar approaches illuminates the limitations of scientific and industry self-regulation. The Asilomar AI process shapes AI’s publicity in three interconnected ways: as an agenda-setting manoeuvre to promote longtermist beliefs; as an approach to policy making that restricts public engagement; and as a mechanism to enhance industry control of AI governance.
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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".