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Updated: Sep 9, 2025

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Armar el sesgo cognitivo en sistemas autónomos: un marco para ataques de inferencia de caja negra

Shiyong Chu1, Yuwei Chen1

  • 1Aviation Industry Development Research Center of China, Beijing, China.

Frontiers in artificial intelligence
|September 5, 2025
PubMed
Resumen

Los sistemas autónomos exhiben sesgos cognitivos debido a las heurísticas de priorización. Nuestro marco de inversión de prioridades a través del razonamiento operativo (PRIOR) revela cómo las señales sutiles hacen que estos sistemas juzguen mal los riesgos, incluso en escenarios simétricos.

Palabras clave:
Vigilancia de los VANTsesgo cognitivoVulnerabilidad a nivel de inferenciaAtaques de caja negra no perturbadoresInversión de prioridadSistemas autónomos confiablesRobustez del razonamiento visual

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Área de la Ciencia:

  • Inteligencia artificial
  • Ciencias cognitivas
  • Sistemas autónomos

Sus antecedentes:

  • Los sistemas autónomos en entornos de alta dimensión utilizan heurísticas de priorización para la atención y la evaluación de riesgos.
  • Estas heurísticas pueden introducir sesgos cognitivos (saliencia, encuadre espacial, familiaridad temporal) que afectan la toma de decisiones.
  • El acceso limitado a los sistemas de visión incorporados requiere métodos de diagnóstico alternativos.

Objetivo del estudio:

  • Introducir la inversión de prioridades a través del razonamiento operativo (PRIOR), un marco de diagnóstico de caja negra.
  • Para sondear las vulnerabilidades de nivel de inferencia en sistemas autónomos utilizando señales estructuralmente sesgadas y semánticamente neutrales.
  • Evaluar el PRIOR utilizando grandes modelos de lenguaje (LLM) como proxies para la priorización cognitiva en escenarios de vigilancia simulados.

Principales métodos:

  • Desarrollado PRIOR, un marco de diagnóstico no perturbador.
  • Empleó señales de escenario estructuralmente sesgadas pero semánticamente neutrales.
  • Utilizó LLM como proxies de razonamiento abstracto para la vigilancia simulada de vehículos aéreos no tripulados (UAV).

Principales resultados:

  • Las señales estructurales mínimas indujeron inversiones de prioridad consistentes en múltiples LLM.
  • El análisis de las justificaciones y las estimaciones de confianza reveló distorsiones sistemáticas en la relevancia de la amenaza inferida.
  • Las entradas simétricas no impidieron estas distorsiones en la lógica de priorización.

Conclusiones:

  • El razonamiento a nivel de inferencia en los sistemas de caja negra es frágil.
  • Las estrategias de evaluación deben interrogar la lógica de priorización interna, no sólo la corrección de la salida.
  • Los hallazgos tienen implicaciones para los agentes dinámicos, incorporados y visualmente conectados a tierra en las implementaciones del mundo real.