Integrating Human Augmentation in the Defence Sphere: an Exploratory Mixed-Methods Study on Ethical Principles
Bibliographic record
Abstract
Abstract Human augmentation is defined as the use of science or technology to modify human performance temporarily, or permanently, to exceed normal physical and/or psychological capabilities of a human body. Our previous work proposed nine ethical principles of human augmentation in the defence context: necessity, human dignity, informed consent, transparency and accountability, equity, privacy, ongoing review, international law, and broader social impact. Here we describe the results of a mixed-methods study using focus groups (N Groups = 9) and a web-based survey among serving military personnel (N Participants = 43) examining how important and appropriate the participants thought the principles were when considering the development, adoption, and implementation of human augmentation technology. This study explores the participants’ stated reasons for their ratings, and the association with indicators of experience and socio-demographic groups. This work provides insights into how the principles can relate to each other at various stages of the technology life cycle, and how they could function together to support a thorough ethical analysis during the implementation of such technology. Following our analysis, several refinements to the principles are subsequently suggested.
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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 | 0.100 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".