Time to Get Serious about Privacy Policies: The Special Case of Genetic Privacy
Bibliographic record
Abstract
Abstract Genetic information is widely recognised as being particularly sensitive personal information about an individual and his or her family. This article presents an analysis of the privacy policies of Australian companies that were offering direct-to-consumer genetic testing services in 2012–13. The results of this analysis indicate that many of these companies do not comply with the Privacy Act 1988 (Cth), and will need to significantly reassess their privacy policies now that significant new amendments to the Act have come into force. Whilst the Privacy Commissioner has increased powers under the new amendments, the extent to which these will mitigate the deficiencies of the current regime in relation to privacy practices of direct–to-consumer genetic testing companies remains unclear. Accordingly, it may be argued that a privacy code for the direct-to-consumer genetic testing industry would provide clearer standards. Alternatively it may be time to rethink whether a sui generis approach to protecting genetic information is warranted.
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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 | 0.061 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.024 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".