Moral Enhancement Meets Normative and Empirical Reality: Assessing the Practical Feasibility of Moral Enhancement Neurotechnologies
Bibliographic record
Abstract
Moral enhancement refers to the possibility of making individuals and societies better from a moral standpoint. A fierce debate has emerged about the ethical aspects of moral enhancement, notably because steering moral enhancement in a particular direction involves choosing amongst a wide array of competing options, and these options entail deciding which moral theory or attributes of the moral agent would benefit from enhancement. Furthermore, the ability and effectiveness of different neurotechnologies to enhance morality have not been carefully examined. In this paper, we assess the practical feasibility of moral enhancement neurotechnologies. We reviewed the literature on neuroscience and cognitive science models of moral judgment and analyzed their implications for the specific target of intervention (cognition, volition or affect) in moral enhancement. We also reviewed and compared evidence on available neurotechnologies that could serve as tools of moral enhancement. We conclude that the predictions of rationalist, emotivist, and dual process models are at odds with evidence, while different intuitionist models of moral judgment are more likely to be aligned with it. Furthermore, the project of moral enhancement is not feasible in the near future as it rests on the use of neurointerventions, which have no moral enhancement effects or, worse, negative effects.
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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 | 0.112 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".