The Ethics of Neuroenhancement
Bibliographic record
Abstract
According to several recent studies, a big chunk of college students in North America and Europe uses so called ‘smart drugs' to enhance their cognitive capacities aiming at improving their academic performance. With these practices, there comes a certain moral unease. This unease is shared by many, yet it is difficult to pinpoint and in need of justification. Other than simply pointing to the medical risks coming along with using non-prescribed medication, the salient moral question is whether these practices are troubling in and of themselves. In due consideration of empirical insights into the concrete effects of smart drugs on brain and behavior, our attempt is to articulate wherein this moral unease consists and to argue for why the authors believe cognitive enhancement to be morally objectionable. The authors will contend that the moral problem with these practices lies less in the end it seeks, than in the underlying human disposition it expresses and promotes. Some might ask, what is wrong with molding our cognitive capacities to achieve excellence, get a competitive edge, or, as the whim takes us? In all of these occasions, the usage of smart drugs serves a certain goal, a telos. The goal is, broadly speaking, this: outsmarting opponents in an arms race for limited resources and thereby yielding a competitive edge. In plain words: competition is valued higher than cooperation or solidarity. What is wrong with striving for this goal? The authors submit that the question whether people really want to live in a society that promotes the mentality ‘individual competition over societal cooperation' deserves serious consideration. In developing their answer, the authors draw on an ‘Ethics of Constraint' framework, arguing that widespread off-label use of smart drugs bears the risk of negative neural/behavioral consequences for the individual that might, in the long run, be accompanied by changing social value orientations for the worse.
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How this classification was reachedexpand
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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".