Faster, Higher, More Moral: Human Enhancement and Christianity
Bibliographic record
Abstract
The three authors of this article explore the intersection of moral enhancement, ethics, and Christianity. Trothen reviews the meaning and potential of moral enhancements, considering some of the risks and limitations. Trothen identifies three broad ethical questions, which all three authors agree upon, that arise from a Christian theological perspective: what it means to be human, choice, and social justice. Trothen concludes that respect for human dignity and social justice requires rejecting a reductive view of moral improvement as purely biochemical. Buttrey then argues that biomedical moral enhancement (BME) is simply one in a series of attempts to morally improve human beings and can be compared to other efforts such as neo-Aristotelian virtue ethics. He argues that BME cannot be simultaneously more reliable than moral education in virtue and no more restrictive of human freedom. He concludes by suggesting that tensions between BME and Thomistic virtue are even stronger due to Christian conceptions of martyrdom and radical self-denial. Finally, McQueen argues that Christianity emphasizes the common good and social justice as essential for human flourishing. Building on the foundation established by Trothen and Buttrey, McQueen insists that accurate cognitive knowledge is needed to make good conscience decisions, but emphasizes that right human action also requires the exercise of the will, which can be undermined by AI, automation, and perhaps also BME. She concludes by encouraging further attention to the true nature of human agency, human freedom, and wisdom in debates over AI and biomedical enhancement. The authors conclude that BMEs, if they become medically safe, may be theologically justifiable and helpful as a supplement to moral improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".