Potential for Genetic Discrimination in Access to Insurance: Is There a Dark Side to Increased Availability of Genetic Information?
Bibliographic record
Abstract
This article addresses the reliance on genetic information as part of the private insurance industry’s practice of risk segmentation whereby underwritingdecisions are based on risk information about individuals and groups as compared to the general population. The author argues that there are a number of concerns regarding reliance on genetic information in insurance underwriting, including uncertainty about what constitutes genetic information and the predictive value thereof, possible conflicts with human rights values, potential reductions in access to insurance, and the legal and ethical obligations of individuals who undergo testing, health professionals, and insurers. This article reviews the solutions that have been adopted in other jurisdictions and concludes that the use of genetic information is consistent with standard insurance industry practices. However, it is recommended that a legislative framework be established in Canada to regulate the use of genetic information.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".