Biomedical Data Identifiability in Canada and the European Union: From Risk Qualification to Risk Quantification?
Bibliographic record
Abstract
Data identifiability standards in Canada and the European Union rely on the same concepts to distinguish personal data from non-personal data. However, courts have interpreted the substantive content of such metrics divergently. Interpretive ambiguities can create challenges in determining whether data has been successfully anonymised in one jurisdiction, and whether it would also be considered anonymised in another. These difficulties arise from the law’s assessment of re-identification risk in reliance on qualitative tests of ‘serious risk’ or ‘reasonable likelihood’ as subjectively appreciated by adjudicators. We propose the use of maximum re-identification risk thresholds and quantitative methodologies to assess data identifiability and data anonymisation relative to measurable standards. We propose that separate legislation be adopted to address data-related practices that do not relate to demonstrably identifiable data, such as algorithmic profiling. This would ensure that regulators do not expand the jurisprudential conception of identifiable data purposively to capture such practices.
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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 | 0.184 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.016 | 0.054 |
| Scholarly communication | 0.036 | 0.019 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".