Privacy in Direct-to-Consumer Genetic Testing
Bibliographic record
Abstract
Over the past decade, direct-to-consumer (DTC)6 genetic testing has grown from an intellectual curiosity to a mainstream technology. In 2018, the total number of US customers for DTC genetic testing has been predicted to exceed 20 million. There are many benefits to DTC genetic testing, including increased accessibility of testing at rapidly decreasing costs. A new powerful use of DTC genetic testing data is the identification and surveillance of persons of interest. In 2018, a suspect in the Golden State Killer murder cases was identified by use of DTC genetic testing information, 30 years after the last known crime was committed. Since the break in the Golden State Killer cases, >100 law enforcement investigations are now underway using DTC genetic data. Individuals providing DTC genetic data may not be aware their data are being used for law enforcement activity to identify links with distant relatives. There are currently minimal regulations and ethical standards to guide the appropriate use of consumer genetic databases by law enforcement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".