The Use of AI and Robotics in Armed Conflicts
Bibliographic record
Abstract
This systematic literature review (SLR) explores existing and newly emergent ethical and legal challenges associated with the use of AI and robotics in armed conflicts. We conducted an extensive review of relevant scholarly publications associated with (lethal) autonomous weapons systems (LAWS). Besides the ethical and legal principles, we also explore emergent technical applications associated with these technologies in armed conflict(s). Our particular focus is to compare literature from the last 12 years with publications since the outbreaks of recent armed conflicts from the perspective of LAWS. We engage in exploring and identifying the shifts in ethical arguments and discourse, as well as shifts in policy subject themes, and standards setting around the use of emergent technology in relation with AI and robotics. Our contribution analyses emergent socio-technical themes and arguments relevant for engineers, policy-makers, and other interdisciplinary scholars across a variety of disciplines.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".