Analyzing the Human Rights Impact of Increased Digital Public Health Surveillance during the COVID-19 Crisis
Bibliographic record
Abstract
The COVID-19 pandemic has led policy makers to expand traditional public health surveillance to take advantage of new technologies, such as tracking apps, to control the spread of SARS-CoV-2. This article explores the human rights dimensions of how these new surveillance technologies are being used and assesses the extent to which they entail legitimate restrictions to a range of human rights, including the rights to health, life, and privacy. We argue that human rights offer a crucial framework for protecting the public from regulatory overreach by ensuring that digital health surveillance does not undermine fundamental features of democratic society. First, we describe the surveillance technologies being used to address COVID-19 and reposition these technologies within the evolution of public health surveillance tools and the emergence of discussions concerning the compatibility of such tools with human rights. We then evaluate the potential human rights implications of the surveillance tools being used today by analyzing the extent to which they pass the tests of necessity and proportionality enshrined in international human rights law. We conclude by recommending ways in which the harmful human rights effects associated with these technologies might be reduced and public trust in their use enhanced.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".