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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".