The anatomy of AI policies: a systematic comparative analysis of AI policies across the globe
Bibliographic record
Abstract
Abstract The rapid expansion of artificial intelligence (AI) across sectors brings significant benefits but also substantial risks, such as bias, discrimination, and lack of transparency. Mitigating these risks requires AI governance frameworks that ensure ethical and responsible use. While existing studies highlight strategies and ethical guidelines, comparative analyses of emerging responsible AI (RAI) frameworks, standards, and regulations remain limited. This study aims to fill this gap by employing a rapid review methodology to examine 17 responsible AI frameworks, standards, and regulations which we named as AI policies throughout this research, from diverse regions, including Singapore, the US, the UK, Canada, Hiroshima, and Australia, and global organizations including the Organization for Economic Co-operation and Development (OECD), and International Organization for Standardization (ISO). This research aimed to address four primary questions on identifying global and local AI policies, identifying and analyzing their key features, assessing implementation challenges, and determining the essential components for designing an integrated AI governance framework. There are eleven key features identified, including RAI Principles, Stakeholders, Stages (AI software development life cycle), Targeted audiences, Scalability, Enforce-ability, Resource Intensive, Region, Technology, AI governance practices (Prerequisites, outcomes, implementation tools or guides), and AI governance area. The comparative analysis highlighted that while the AI policies offer detailed implementation guidelines, they differ in their approaches, mandatory nature, scalability, and resource demands. These differences are critical for organizations seeking to implement these policies effectively. Challenges related to resource intensity, scalability, governance practices, and ambiguous targeted audiences were noted as significant barriers to successful adoption. Based on the analysis, key components for an RAI framework were proposed, and categorized into qualities (scalable, extensible, adaptive, efficient), dimensions (scope, context, implementation practices), and governance practices (prerequisites/outcomes, resources, governance steps). These components aim to guide organizations in developing AI governance frameworks.
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 | 0.062 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".