Governing Artificial Intelligence: Designing Professional Structures for the Predictive Age
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
Abstract The consensus on the need to regulate artificial intelligence is clear, but the how remains elusive. Private regulation, as proposed by the tech industry itself, and state regulation, as embodied in the recent EU Artificial Intelligence Act, are two common forms of governance. We advance a third option that has received very little attention to date: professional regulation. Professional regulation is modeled after hybrid public-private regulatory structures found in medicine, such as those put forth by the American Medical Association. Such governance schemes develop both technical and ethical standards, shaping professional training, continuing knowledge, and conduct. We contend that it is the most practical means of ensuring the development of human-centered AI in an era of rapid technological change and intensely opposing views of what regulation ought to do. This article places the responsibility of acting ethically on the group that knows the technology best and can anticipate its effects: AI developers. But unlike other voluntary standards, professional regulation articulates and enforces standards to certify individuals. Professional licensing is an alternative that provides public protections based on privately developed standards that ensure the safety of AI prior to their release.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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