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La ciencia y la práctica de la proporcionalidad en las evaluaciones de riesgos de la IA
Carlos Mougan1, Lauritz Morlock1, Jair Aguirre2
1European AI Office, European Commission, Brussels, Belgium.
Science (New York, N.Y.)
|February 19, 2026
Resumen
Las evaluaciones de IA deben ofrecer información útil sobre los riesgos sin crear un trabajo innecesario. Esto garantiza que la evaluación de riesgos de la inteligencia artificial sea práctica y eficiente para todos los usuarios.
Área de la Ciencia:
- Inteligencia Artificial
- Gestión de Riesgos
- Metodologías de Evaluación
Sus antecedentes:
- La creciente integración de la inteligencia artificial (IA) requiere marcos de evaluación sólidos.
- Los métodos actuales de evaluación de la IA pueden imponer cargas significativas, lo que dificulta su adopción generalizada y su aplicación práctica.
- Existe la necesidad de estrategias de evaluación de riesgos de IA que equilibren la exhaustividad con la eficiencia.
Objetivo del estudio:
- Explorar métodos para evaluaciones de IA que produzcan información significativa sobre riesgos.
- Investigar enfoques que minimicen la carga asociada con la evaluación de riesgos de IA.
- Proponer un marco equilibrado para la evaluación de sistemas de IA.
Principales métodos:
- Revisión de los protocolos de evaluación de IA existentes.
- Análisis de las estrategias de comunicación de riesgos en IA.
- Desarrollo de un modelo conceptual para la evaluación eficiente de riesgos de IA.
Principales resultados:
- Se identificaron los componentes clave de la información significativa sobre riesgos para la IA.
- Se caracterizaron las cargas comunes en los procesos actuales de evaluación de la IA.
- Se propuso un enfoque simplificado centrado en los indicadores de riesgo de alto impacto.
Conclusiones:
- Las evaluaciones efectivas de IA pueden proporcionar información crítica sobre los riesgos.
- Minimizar la carga de la evaluación es crucial para la implementación práctica de la IA.
- Un enfoque equilibrado mejora la utilidad y la viabilidad de la gestión de riesgos de IA.
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