Applying Bostrom's Reversal Test to check the Principle of Procreative Beneficence's major critiques for the Status Quo Bias
Bibliographic record
Abstract
Julian Savulescu believes parents have a moral duty to use reproductive technologies like IVF and Prenatal screening to choose the best possible child. According to his principle of Procreative Beneficence, one should select the best child of the possible children one could have. However, this principle has attracted numerous critiques from numerous authors. This paper aims to demonstrate that most critiques suffer from a status quo bias. It means that these critiques overly emphasize the possible negative outcomes concerning the principle of Procreative Beneficence because these critiques have an implicit affinity toward the status quo. The affinity for the status quo renders these critiques unable to appreciate the potential positive outcomes of applying the principle of Procreative Beneficence. Some authors argue that these critiques overemphasize the potential negative outcomes. I employ Nick Bostrom's Reversal Test to check these critiques for implicit Status Quo Bias. In Bostrom's Reversal Test, we consider the desired trait, often a positive deviation from the status quo. Suppose we find selecting the embryo with the desired trait ethically contentious. In that case, we imagine selecting an embryo that lacks that desired trait and is a negative deviation from the Status Quo. If we find the latter also problematic, we conclude that choosing the embryo with the desired trait seems ethically contentious because of our affinity to the Status Quo, also called the Status Quo Bias. The thesis accomplishes two tasks. First, it analyzes the various critiques for the Principle of Procreative Beneficence. Second and last, it employs Bostrom's Reversal Test to check these critiques for any potential Status Quo Bias and concludes that PPB’s primary critiques do indeed suffer from the Status Quo Bias.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".