The impact of artificial intelligence on the strategic planning of economic development of countries
Bibliographic record
Abstract
Traditional economic planning frameworks struggle to address rapid market changes and nonlinear sectoral interactions, often resulting in suboptimal policy outcomes. This study systematically analyzes how artificial intelligence (AI) transforms strategic economic development across ten countries (the UK, Japan, the USA, China, Ukraine, France, Canada, Singapore, Germany, and South Korea) from 2015 to 2024. Using a mixed-methods approach – integrating panel data regression (fixed-effects and 2SLS models) with a PRISMA-guided review of 89 studies – the research quantifies AI’s macroeconomic impacts and ethical risks. Key findings reveal that a 1-unit increase in AI adoption intensity correlates with a 0.38–0.41% GDP growth rise, driven by predictive analytics in advanced economies like the USA and Singapore. However, infrastructural gaps in Ukraine caused 31% data loss in AI models, hindering policy scalability. Ethical challenges include algorithmic bias in France’s hiring systems (13% minority recruitment disparity) and data privacy breaches in Singapore (19% corporate breach rate). For Ukraine, targeted recommendations include prioritizing AI-ready digital infrastructure (e.g., centralized data hubs) and adopting EU-style ethical audits to mitigate bias in public-sector algorithms. Policymakers globally must balance AI-driven efficiency with equitable governance to harness its full potential.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".