AI policies across the globe: Implications and recommendations for libraries
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
This article examines the proposed artificial intelligence policies of the USA, UK, European Union, Canada, and China, and their implications for libraries. As artificial intelligence revolutionizes library operations, it presents complex challenges, such as ethical dilemmas, data privacy concerns, and equitable access issues. The article highlights key themes in these policies, including ethics, transparency, the balance between innovation and regulation, and data privacy. It also identifies areas for improvement, such as the need for specific guidelines on mitigating biases in artificial intelligence systems and navigating data privacy issues. The article further provides practical recommendations for libraries to engage with these policies and develop best practices for artificial intelligence use. The study underscores the need for libraries to not only adapt to these policies but also actively engage with them, contributing to the development of more comprehensive and effective artificial intelligence 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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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