Deadly Automatic Systems: Ethical And Legal Problems
Bibliographic record
Abstract
Artificial intelligence, neural networks, speech and behavior recognition systems, drones, autonomous robotic systems - all of these and many other technologies are widely used by the military to create a new type of lethal weapon programmed to independently decide to use military force. According to experts, production of such weapons will be a revolution in military affairs, the same kind of revolution that the creation of nuclear weapons made back in the days. Adoption of fully autonomous combat systems raises a number of ethical and legal issues, the major of which is a destruction of a supposed enemy’s manpower by a robot without a human command. This article focuses on the legal aspects of creating autonomous combat systems, their legal status and the prospects of creating an international document prohibiting lethal robotic technologies. As the result of the study, the authors came to a conclusion that there is no direct legal restriction on the use of fully autonomous combat systems, however, the use of such weapons contradicts the doctrinal norms of international law. The authors also believe that a comprehensive ban on the development, use and distribution of robotic technologies is hardly possible in the foreseeable future. The most possible scenario for solving the problem at an international level is only a ban on the use of this type of military equipment directly during an operational activity of an armed conflict. At the same time, the authors consider it necessary to outline the acceptable areas of application of robotic technologies: medical and logistical support of military operations, military construction, the use of mine clearing robots and similar humanistically justified measures.
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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 | 0.041 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".