Bibliographic record
Abstract
Abstract Knowledge about the relationship between genes and health, as well as genes and behaviour, is expanding rapidly. Genetic and other predictive tests are becoming less expensive and so knowledge about individuals’ genetic backgrounds is likely to become commonplace. Although such information holds the promise of eventually being very useful in the context of improving health outcomes for individuals, it has long been recognised that this promise may be accompanied by the threat of increased genetic discrimination, most notably in insurance markets. Genetic discrimination may also arise in personal and social spheres of life. Each aspect creates different types of problems. Thus, different types of remedies or responses are in order. This article reviews the ethical and economic aspects of this new information from a variety of normative perspectives and considers the appropriateness of the sorts of regulations recently put in place to limit its adverse social implications. Key Concepts: Genetic information can lead to improved decision‐making and treatments for people's health but may also lead to discrimination in insurance markets and beyond. Many academics and members of the public believe that charging different prices to different people based on genetic test results is unfairly discriminatory. However, many insurance market analysts believe individuals at higher risk of death or higher need of healthcare should pay more for insurance regardless of what causes their higher risk status. Economic arguments can be made that not charging risk‐relevant prices for insurance will discourage ‘better’ risks from participating in the market for insurance and this will lead to higher overall prices for insurance. The conflicting views noted above have led to unresolved issues regarding the appropriate regulation of genetic information in the area of insurance pricing. Genetic discrimination may also increase in areas of personal or social relations where market regulations are not effective remedies. Education about genetic differences may be an appropriate alternative mechanism for dealing with genetic discrimination in the personal and social spheres.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.017 |
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".