Digital Surveillance of COVID-19: Privacy and Equity Considerations
Bibliographic record
Abstract
In this paper, we examine the potentially deleterious effects of surveillance on vulnerable Canadians. A wide range of digital surveillance technologies have either been deployed or considered for deployment both in Canada and around the world in response to the international emergency created by the COVID-19 pandemic. Some of these technologies are highly effective in predicting or identifying individual cases and/or outbreaks; others assist in tracing contacts or enforcing compliance with quarantine and isolation measures. However, there are necessarily risks associated with their deployment. First are the infringements on privacy rights of citizens and groups. Second, these technologies run the risk of ‘surveillance creep’ in the context of their desired usage for purposes and in time frames other than for fighting a pandemic. Third, some of these technologies impact more severely on members of racialized and socioeconomically disadvantaged groups. We argue that, without addressing the impact that digital technologies have on vulnerable populations in relation to COVID-19, legislators risk deepening the inequalities that create the very conditions for transmission of the virus and that put vulnerable persons at greater risk of contracting the disease.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".