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Record W2896232791 · doi:10.1017/s1358246118000292

Moral Enhancement as a Collective Action Problem

2018· article· en· W2896232791 on OpenAlexaff
Walter Glannon

Bibliographic record

VenueRoyal Institute of Philosophy Supplement · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHarmSkepticismPsychologyAction (physics)Social psychologyInterpersonal communicationSet (abstract data type)EpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract In light of the magnitude of interpersonal harm and the risk of greater harm in the future, Ingmar Persson and Julian Savulescu have argued for pharmacological enhancement of moral behaviour. I discuss moral bioenhancement as a set of collective action problems. Psychotropic drugs or other forms of neuromodulation designed to enhance moral sensitivity would have to produce the same or similar effects in the brains of a majority of people. Also, a significant number of healthy subjects would have to participate in clinical trials testing the safety and efficacy of these drugs, which may expose them to unreasonable risk. Even if the drugs were safe and effective, a majority of people would have to co-operate in a moral enhancement programme for such a project to succeed. This goal would be thwarted if enough people opted out and decided not to enhance. To avoid this scenario, Persson and Savulescu argue that moral enhancement should be compulsory rather than voluntary. But the collective interest in harm reduction through compulsory enhancement would come at the cost of a loss of individual freedom. In general, there are many theoretical and practical reasons for scepticism about the concept and goal of moral enhancement.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.120
GPT teacher head0.353
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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