Lethal Autonomous Weapons Systems (LAWS)
Bibliographic record
Abstract
La incorporación de la inteligencia artificial en el ámbito bélico ha impulsado el desarrollo de Sistemas de Armas Autónomas Letales (LAWS), generando serios desafíos para la regulación internacional. Este artículo analiza las tensiones conceptuales y normativas que rodean su despliegue, destacando la falta de definiciones claras y las disputas entre enfoques de soft law y hard law. A partir del estudio de actores como Estados Unidos, Rusia, China, Israel y la Unión Europea, se evidencia un consenso emergente sobre la necesidad de garantizar un “control humano significativo” en los sistemas de armas. Sin embargo, persisten fracturas que obstaculizan acuerdos efectivos. Ante la parálisis de foros multilaterales como la Convención sobre Ciertas Armas Convencionales (CCW), se propone explorar esquemas alternativos de gobernanza inspirados en tratados como el de Ottawa y el de Prohibición de Armas Nucleares, que permitan avanzar hacia una regulación robusta y urgente de los LAWS.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".