Privacy, Exploitation and Global Disease Surveillance: Can We Justly Prevent the Next Pandemic?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.006 |
| 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 | 0.000 | 0.000 |
| 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