The What, So What, and What Now of the AI Landscape in Emerging Economies
Bibliographic record
Abstract
Artificial intelligence (AI) rapidly transforms global dynamics, significantly impacting various industries and societal sectors. In this systematic review, we explore the state of integration and challenges impeding the adoption of AI in the Global South, a region facing unique developmental and economic challenges. Our analysis of 100 academic literature reviews AI's adoption in these regions across healthcare, education, agriculture, manufacturing, services, and media sectors, identifying key research gaps and regional challenges, including ethical issues and the digital divide. While AI holds transformative potential, its adoption in these regions is hindered by infrastructural deficiencies, skill shortages, and complex policy landscapes. We then offer targeted recommendations for policymakers, academia, entrepreneurs, and NGOs to tailor AI solutions to local needs to address these barriers and enhance economic and social equity. Our review emphasizes the need for collaborative efforts to ensure that AI fosters inclusive growth and sustainable development in the Global South.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".