AI policies across the globe: Implications and recommendations for libraries
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.
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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 | 0.042 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.053 | 0.044 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".