Computer Vision Applications and their Ethical Risks in the Global South
Why this work is in the frame
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Bibliographic record
Abstract
We present a study of recent advances in computer vision (CV) research for the Global South to identify the main uses of modern CV and its most significant ethical risks in the region. We review 55 research papers and analyze them along three principal dimensions: where the technology was designed, the needs addressed by the technology, and the potential ethical risks arising following deployment. Results suggest: 1) CV is most used in policy planning and surveillance applications, 2) privacy violations is the most likely and most severe risk to arise from modern CV systems designed for the Global South, and 3) researchers from the Global North differ from researchers from the Global South in their uses of CV to solve problems in the Global South. Results of our risk analysis also differ from previous work on CV risk perception in the West, suggesting locality to be a critical component of each risk's importance.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it