Artificial intelligence and the politics of avoidance in global health
Bibliographic record
Abstract
For decades, global health actors have centered technology in their interventions. Today, artificial intelligence (AI) is emerging as the latest technology-based solution in global health. Yet, AI, like other technological interventions, is not a comprehensive solution to the fundamental determinants of global health inequities. This article gathers and critically appraises grey and peer-reviewed literature on AI in global health to explore the question: What is avoided when global health prioritizes technological solutions to problems with deep-seated political, economic, and commercial determinants? Our literature search and selection yielded 34 documents, which we analyzed to develop seven areas where AI both continues and disrupts past legacies of technological interventions in global health, with significant implications for health equity and human rights. By focusing on the power dynamics that underpin AI's expansion in global health, we situate it as the latest in a long line of technological interventions that avoids addressing the fundamental determinants of health inequities, albeit at times differently than its technology-based predecessors. We call this phenomenon the 'politics of avoidance.' We conclude with reflections on how the literature we reviewed engages with and recognizes the politics of avoidance and with suggestions for future research, practice, and advocacy.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.104 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| 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".