AI Literacy and Governance as Foundations for Ethical AI: A Cross-National Review of Government Strategies
Bibliographic record
Abstract
The increasing deployment of artificial intelligence (AI) across various sectors has prompted nations to develop comprehensive strategies for governing AI technologies, with a growing emphasis on improving ethical AI practices. This paper presents a cross-national review of government AI strategies by focusing on how AI literacy and governance frameworks contribute to the ethical deployment of AI. By analyzing AI strategies from five regions: the United States of America, the European Union, China, Canada, and Singapore, the paper explores how these governments address the challenge of building AI literacy and governance mechanisms to support responsible AI development. The study examines the role of AI literacy in empowering stakeholders—including policymakers, developers, and the general public—to understand, evaluate, and ethically engage with AI systems. The review identifies best practices, governance mechanisms, and gaps in national strategies and recommends strengthening AI literacy and governance as foundational elements for ethical AI. This research contributes to the global discourse on AI ethics by highlighting the critical role of literacy and policy in promoting trustworthy and socially beneficial AI deployment.
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How this classification was reachedexpand
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".