Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries
Bibliographic record
Abstract
Infectious diseases continue to pose a significant global health challenge, necessitating innovative approaches for predicting outbreaks, detecting variants, conducting contact tracing, discovering new drugs and managing misinformation. Artificial intelligence (AI) has significantly supported work in these areas, particularly during the COVID-19 pandemic. However, the benefits of AI must be equitably distributed, and its use must be responsible and inclusive. As various nations implement AI regulations, the global nature of AI necessitates international collaboration to establish ethical guidelines and governance frameworks. In response to these needs, the Global South AI for Pandemic & Epidemic Preparedness & Response Network (AI4PEP) is leading a multinational effort across 16 countries to strengthen public health systems through responsible, Southern-led AI solutions. This opinion piece highlights AI4PEP's initiatives in Latin America and the Caribbean (LAC), examining the region's AI governance models and the challenges they present. By lowering barriers to AI adoption and fostering equitable access to AI-driven public health innovations, our network empowers researchers, healthcare professionals and policymakers in LAC to harness AI for infectious disease preparedness and response, ultimately improving health outcomes in low- and middle-income countries.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".