Epistemic and ethical considerations in the direct-to-consumer health and ancestry genetic testing process
Bibliographic record
Abstract
Background: Direct-to-consumer genetic testing (DTC-GT) is a popular and fast-growing field within the healthcare industry. Consumers often pursue DTC-GT without a clear understanding of its epistemic and medical limitations. This report will present the current state of DTC-GT technology, and highlight the ethical, legal and social issues of DTC-GT. Methods: Quantitative methods such as systematic reviews were used to evaluate the field of DTC-GT. Experimental data was taken from randomized control trials and case studies of 23andMe. Qualitative methods such as newspaper articles and surveys were also used. Relevant policies and regulatory information were analyzed in the context of 23andMe. Broader ethical issues are analyzed from the social disability model and feminist ethics frameworks. Results: Several aspects of direct-to-consumer genetic testing are outlined: (i) regulatory and legal distinctions of DTC-GT that separate its use from conventional genetic testing, (ii) epistemic issues of the genetic testing process within the direct-to-consumer context, and (iii) ethical considerations of DTC-GT in regard to genetic health and genetic ancestry. Conclusion: This report does not take a position for or against the use of DTC-GT; rather, it highlights the key ethical issues often missed in the DTC-GT process. There is no perfect method for understanding genetic health and race. DTC-GT offer consumers the ease and power of taking genetic data ‘in their own hands’, at the cost of exacerbating geneticization and race essentialism. Until further work is done to address the epistemic, regulatory and legal issues, ethical implications of DTC-GT usage will continue to exist.
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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 | 0.147 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.064 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".