General Legal Limits of the Application of the Lethal Autonomous Weapons Systems within the Purview of International Humanitarian Law
Bibliographic record
Abstract
This article focuses on the problem of regulation of the application of the autonomous weapons systems from the perspective of the norms and principles of international humanitarian law. The article discusses the question of what restrictions are imposed on the application of such weapons in the international humanitarian law. The article presents a number of principles that must be met by both the weapons and their method of their application: distinction between civilians and combatants, military necessity, proportionality, prohibition on causing unnecessary suffering, and humanity. The author concludes that from the perspective of the principles of the international humanitarian law, it is doubtful if autonomous systems would be able to comply with these principles. Weapons that hit targets without human intervention have been applied for a long time, but they have never had the independence that they have now. The issue of compliance of autonomous weapons systems with the international humanitarian law can be considered if sufficient experience of application of such weapons in real conditions is accumulated. This study demonstrates that it is impossible to say that autonomous weapons systems do not comply with the principles of humanitarian law in general. The paper provides policy recommendations and assessments for each of the principles under consideration. The author also concludes that it would be necessary not to prohibit autonomous weapons, because they do not comply with the principles of international humanitarian law, but to develop rules for their application and for human participation in their functioning. A significant challenge to the development of such rules is the opacity of these autonomous weapons systems, if we look at them as at the complex intelligent computer systems.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".