Talking to a brick wall: The US government's response to public comments on AI
Bibliographic record
Abstract
Building trust in artificial intelligence (AI) is an elusive goal, especially if AI models are closed or partially open, making it difficult for users to determine if these models are reliable, fair or trustworthy. For this reason, the Biden administration sought public input on the potential risks and benefits of these models as well as policy approaches. In an executive order, he tasked the assistant secretary of commerce for communications and information (who was also head of the National Telecommunications and Information Agency [NTIA]) to solicit feedback through a public consultation process. NTIA advises the president on information, telecommunications and related technology policy, including AI. The author used a landscape analysis to examine the dialogue between US officials and the public response. Although some 300 Americans participated in the dialogue, these commenters did not provide a representative sample of Americans who use or might be affected by open versus closed AI systems. Those who did provide their opinions likely had a direct stake in these issues. The dialogue was also dysfunctional because policy makers did not really listen to - or even report on - what they heard.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".