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Lecciones de la ciencia de sistemas complejos para la gobernanza de la IA

Noam Kolt1,2, Michal Shur-Ofry1, Reuven Cohen3

  • 1Faculty of Law, Hebrew University, Jerusalem, Israel.

Patterns (New York, N.Y.)
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PubMed
Resumen
Este resumen es generado por máquina.

Los principios de los sistemas adaptativos complejos ofrecen ideas cruciales para gobernar la inteligencia artificial (IA). Aplicar estas lecciones ayuda a manejar la IA

Palabras clave:
Riesgos en cascadaSistemas adaptativos complejossurgimientobucles de retroalimentaciónregulación y gobernanzaEscalamiento

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Área de la Ciencia:

  • Aplicaciones interdisciplinarias de la ciencia de los sistemas adaptativos complejos.
  • Integración de ideas de la física, la biología y las ciencias sociales para la gobernanza de la IA.
  • Aprovechar la teoría de sistemas complejos para comprender el comportamiento y los riesgos de la IA.

Sus antecedentes:

  • Los sistemas contemporáneos de inteligencia artificial (IA) exhiben características de sistemas adaptativos complejos.
  • Los entornos de IA muestran crecimiento no lineal, fenómenos emergentes y fallas en cascada.
  • Los desafíos en la gobernanza de la IA se derivan de los circuitos de retroalimentación y las interdependencias de la infraestructura crítica.

Objetivo del estudio:

  • Explorar la aplicabilidad de los principios de los sistemas adaptativos complejos a la gobernanza de la IA.
  • Identificar los desafíos clave en la gobernanza de la IA iluminados por la ciencia de los sistemas complejos.
  • Proponer un marco para la gobernanza de la IA compatible con la complejidad.

Principales métodos:

  • Dibujando paralelismos entre los sistemas adaptativos complejos y el comportamiento de la IA.
  • Analizar los desafíos de la gobernanza de la IA a través de la lente de los sistemas complejos.
  • Examinar estudios de casos de salud pública y cambio climático para obtener información sobre la gobernanza.

Principales resultados:

  • Los sistemas de IA y sus entornos comparten propiedades con sistemas adaptativos complejos.
  • La profunda incertidumbre caracteriza los esfuerzos actuales para gobernar la IA.
  • Las características específicas de la IA, como los bucles de retroalimentación de datos sintéticos, plantean desafíos de gobernanza.

Conclusiones:

  • La gobernanza de la IA requiere principios compatibles con sistemas adaptativos complejos.
  • Los datos deseados propuestos incluyen la intervención temprana/escalable, las instituciones adaptativas y los umbrales de riesgo calibrados.
  • La gobernanza efectiva de la IA requiere estrategias adaptativas para gestionar la incertidumbre y los riesgos emergentes.