AI Technology Policy Comparison: A Content Analysis Based Policy Review and Forecasting
Bibliographic record
Abstract
The development of artificial intelligence (AI) technologies has become a critical factor in technology-leading countries’ emerging technology policy formulation. AI technology policy varies from country to country in multiple dimensions. This research contributes to the contemporary literature on the national innovation system, economic growth, and innovation ecosystem by highlighting the historical and trending characteristics of AI technology policymaking and expanding the analytical framework on technology policy comparison. Empirically, this study employs a content analysis methodology to conduct a comparative review and analysis of AI technology policies across ten countries and territories over a decade (2014–2023). By analyzing 640 policy documents sourced from the OECD AI Policy Observatory, the research identifies commonalities, differences, and policy trends in three dimensions: policy volume, policy themes, and policy target groups. Research findings reveal significant variability in policy priorities and thematic focuses among countries and territories in the past decade. Leading technology countries such as the United States and the United Kingdom lead in policy volume. China and Japan, show balanced policy attention between AI governance and ethical principles. The results also shed light on the interaction between academia, industry, and policymakers by offering insights into the evolving dynamics of AI technology policy strategies, helping policymakers to be prepared for AI ethical and governance challenges at a national level and to form a supportive national innovation ecosystem.
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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 | 0.154 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.104 | 0.088 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".