Privacy Considerations of Location Tracking in Social Welfare Applications
Bibliographic record
Abstract
COVID-19 rapidly spread worldwide starting in December 2019, reaching its peak during the first quarter of 2021. As of October 28, 2021, COVID-19 deaths have surpassed 5,000,000 globally, with the highest death tolls in the United States, Brazil, and India. Governments scrambled to combat the pandemic using various techniques, including social welfare and pandemic tracking applications. This scramble accelerated the progress of the digital revolution through the proliferation of numerous social welfare applications worldwide. These applications are used for tracking vaccination status, contact tracing, social distancing, symptoms, and positive cases, as well as for enforcing quarantine and lockdown policies and detecting violations. Artificial intelligence and other technology advances raise concerns about security, privacy, and equity since many of these applications work with personal information from one’s health records, employment information, and location data. This paper examines such applications in the Kingdom of Saudi Arabia, the Kingdom of Bahrain, and the United States. Develop best practices that can be followed to enhance the security of and equitable access to social applications.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".