Neuroenhancements in the Military: A Mixed-Method Pilot Study on Attitudes of Staff Officers to Ethics and Rules
Bibliographic record
Abstract
Abstract Utilising science and technology to maximize human performance is often an essential feature of military activity. This can often be focused on mission success rather than just the welfare of the individuals involved. This tension has the potential to threaten the autonomy of soldiers and military physicians around the taking or administering of enhancement neurotechnologies (e.g., pills, neural implants, and neuroprostheses). The Hybrid Framework was proposed by academic researchers working in the U.S. context and comprises “rules” for military neuroenhancement (e.g., ensuring transparency and maintaining dignity of the warfighter). Integrating traditional bioethical perspectives with the unique requirements of the military environment, it has been referenced by military/government agencies tasked with writing official ethical frameworks. Our two-part investigation explored the ethical dimensions of military neuroenhancements with military officers – those most likely to be making decisions in this area in the future. In three workshops, structured around the Hybrid Framework, we explored what they thought about the ethical issues of enhancement neurotechnologies. From these findings, we conducted a survey (N = 332) to probe the extent of rule endorsement. Results show high levels of endorsement for a warfighter’s decision-making autonomy, but lower support for the view that enhanced warfighters would pose a danger to society after service. By examining the endorsement of concrete decision-making guidelines, we provide an overview of how military officers might, in practice, resolve tensions between competing values or higher-level principles. Our results suggest that the military context demands a recontextualisation of the relationship between military and civilian ethics.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".