Ready but irresponsible? Analysis of the Government Artificial Intelligence Readiness Index
Bibliographic record
Abstract
Abstract Many are the promises of artificial intelligence (AI) and algorithms. Governments around the world are increasingly investing in AI and multiple voices have touted this seemingly unmatched revolution. Better performance, cost reduction, efficient management, crime prediction, and prevention are but a few of the pledges of the AI era. While such promises are recognized, research shows that AI benefits could be overstated. Issues of equity, ethics, justice, and fairness have raised concerns and have been seen as potentially threatening democratic principles. As countries get ready to tap into the AI power, researchers are asking whether preparedness is followed by responsibility checks. In this article, we use the Oxford Insights AI Readiness Index to explore why innovation and readiness in artificial intelligence are not always accompanied by accountability, even for some of the most advanced democracies around the world. Using the Fuzzy‐Set Qualitative Comparative Analysis (fsQCA) approach, we show that advancement in AI is not enough: privacy, transparency, inclusion, and accountability principles are key to ensuring governments tackle the AI challenge responsibly.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".