Privacy, Exploitation and Global Disease Surveillance: Can We Justly Prevent the Next Pandemic?
Bibliographic record
Abstract
In light of the COVID-19 pandemic, global health organizations have called for the implementation of robust global disease surveillance systems to recognize and respond to emerging pathogens. These active surveillance technologies would have a significant global benefit by preventing the spread of current pandemics and informing future pandemic responses. In this paper, I examine the extent to which we can sacrifice individuals' privacy through global disease surveillance in order to benefit current and future generations. First, I outline disease surveillance technologies and explain how disease surveillance would occur primarily in low-income, rural communities in the Global South. Next, I outline privacy-related harms that these individuals would experience as a result of disease surveillance. I argue that within our current distributional system for global health resources, pandemic surveillance would impose privacy-related burdens on marginalized communities, who would receive inadequate benefits from these programs. This is unfair because it exploits the worst off in order to benefit individuals in wealthy nations. I conclude that to justifiably implement global disease surveillance, we ought to adopt a 'prioritarian' approach to health distribution. To impose privacy-related burdens on the worst off, we must ensure that they benefit significantly.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".