Tracking, tracing, trust: contemplating mitigating the impact of <scp>COVID</scp> ‐19 through technological interventions
Bibliographic record
Abstract
To the Editor: The use of Bluetooth-enabled apps like Australia's COVIDSafe to contact trace people exposed to coronavirus disease 2019 (COVID-19) raises challenging moral and public health questions. Leins and colleagues1 rightly note that such tracing may endanger human rights. Yet the ethical decisions for governments and citizens are complex. The absence of vaccines and effective treatments, and the significant asymptomatic transmission of SARS-CoV-2, compels reliance on traditional tactics of social distancing, quarantine and contact tracing.2, 3 Although the added value of digital contact tracing over manual tracing remains uncertain, even marginal improvements may interrupt disease transmission, save lives and improve public health resourcing. This could especially benefit vulnerable and disadvantaged people who suffer disproportionate harms,4 without treating digital contact tracing as a “silver bullet”. Whether, and which, digital contact tracing options are warranted depends on tough cost–benefit judgements. COVIDSafe's centralised storage of data on Amazon's servers facilitates access by governments with extraordinary power to interfere in citizens’ lives. Alternatively, decentralised data storage on smartphones has privacy advantages — but providing individual app users with the discretion to act on notifications of potential exposure to COVID-19 may compromise disease control efforts. A hard choice exists between allowing personal data to be accessible by democratically elected governments versus powerful technology giants like Apple and Google which support decentralised data storage.5 Even greater invasions of privacy have been proposed, however, with location tracking options such as Norway's Smittestopp app (https://helsenorge.no/coronavirus/smittestopp) promoted as necessary to understand community interactions and the effects of social distancing policies for current (and future) outbreaks. While Leins and colleagues highlight significant ethical drawbacks, a full ethical analysis of digital contact tracing must also weigh its potential benefits. Certainly, citizens should agitate for strong protections to prevent abuse of power and misuse of personal information. However, even when governments offer ethically suboptimal contact tracing options, it may be permissible and even a moral requirement, all things considered, for citizens to support options to help protect the community. For its part, the Australian government should recognise that deploying digital tracing without sufficient transparency and community and expert input leaves citizens with harder moral decisions. No relevant disclosures.
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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 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".