The is and ought of the Ethics of Neuroenhancement: Mind the Gap
Bibliographic record
Abstract
Ethical perspectives on the use of stimulants to enhance human cognitive performance (neuroenhancement) are polarized between conservative and liberal theories offering opposing advice on whether individuals have a right to use neuroenhancers and what the social outcomes of neuroenhancement might be. Meanwhile, empirical evidence shows modest prevalence and guarded public attitudes toward the neuroenhancement use of stimulants. In this Perspective, we argue that the dissonance between the prescriptions of ethical theories (what ought to be) and empirical evidence (what is) has impaired our understanding of neuroenhancement practices. This dissonance is a result of three common errors in research on the ethics of neuroenhancement: (1) expecting that public perspectives will conform to a prescriptive ethical framework; (2) ignoring the socio-economic infrastructures that influence individuals' decisions on whether or not to use neuroenhancement; and (3) overlooking conflicts between fundamental ethical values namely, safety of neuroenhancement and autonomy. We argue that in order to understand neuroenhancement practices it is essential to recognize which values affect individual decisions to use or refuse to use neuroenhancement. Future research on the ethics of neuroenhancement should assess the morally significant values for stakeholders. This will fill the gap between what ought to be done and what is done with an improved understanding of what can be done within a particular context. Clarifying conflicts between competing moral values is critical in conducting research on the efficacy of substances putatively used for neuroenhancement and also on neuroenhancement practices within academic, professional and social environments.
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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 | 0.052 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.125 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".