The Ethics of Pre-implantation Genetic Diagnosis: An Opinion Piece Examining the Moral Distinction Between Positive and Negative Selection of Traits Using PGD
Bibliographic record
Abstract
Pre-implantation genetic diagnosis (PGD) follows in vitro fertilization (IVF) of several ova. Negative selection (NS), or the discarding of embryos containing undesirable alleles, is currently being performed in IVF clinics. Conversely, positive selection (PS) is the discarding of embryos that do not contain a desirable allele. In other words, PS keeps an embryo because it contains a desirable genetic profile. There are many groups that support NS but there are far fewer who support PS. The bioconservative philosophy, led by philosophers such as Leon Kass, opposes PS and bioliberalism in general. Conversely, NS (and PS) of embryos resonates best of all with the bioliberalism philosophy. More specifically, a subset of bioliberalism, called transhumanism. In order to find NS morally permissible and PS morally unacceptable, one must support one’s position by making a moral distinction between the two types of selection. The major claims against PS include that it is not medically serious, that it propagates eugenics, that it propagates sex selection and that it elicits a moral repugnance which proves its immorality. In analyzing these arguments, I hope to show that none of them are consistent in their application, and that their inability to be applied universally significantly weakens their case.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".