Moral Enhancement Meets Normative and Empirical Reality: Assessing the Practical Feasibility of Moral Enhancement Neurotechnologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it