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. How have you been involved in DNA testing, privacy, and/or the use of DNA in law enforcement? Michael T. Risher: I have been litigating challenges to laws that require people arrested on suspicion of a felony to provide DNA samples for inclusion in the government's Combined DNA Index System (CODIS) database since 2009, first as a staff attorney with the American Civil Liberties Union of Northern California and now in private practice. I have written and testified before Congress about the privacy and racial-justice implications of the increasing use of criminal DNA databanks and familial searching. Timothy Caulfield: Our team at the Health Law Institute at the University of Alberta has been looking at these issues for decades. This has included work on social issues associated with DTC genetic testing, the collection of samples …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".