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Recursos algorítmicos en la toma de decisiones secuenciales para la equidad a largo plazo.

Francisco Gumucio1, Lu Zhang1

  • 1Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR, United States.

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Resumen
Este resumen es generado por máquina.

Es difícil lograr la equidad a largo plazo en decisiones secuenciales. Este estudio introduce el recurso algorítmico causal secuencial para la equidad (SCARF) para crear planes personalizados que reduzcan las disparidades a lo largo del tiempo.

Palabras clave:
El recurso algorítmico.El contrafactual es el contrafactual.equidad a largo plazo.toma de decisiones secuencialmente.modelos estructurales de causalidad.

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Área de la Ciencia:

  • La inteligencia artificial es inteligencia artificial.
  • Ciencias de la computación Ciencias de la computación
  • Aprendizaje automático Aprendizaje automático.

Sus antecedentes:

  • La toma de decisiones secuencial plantea desafíos para la equidad a largo plazo, ya que las elecciones tempranas pueden empeorar las disparidades.
  • Los métodos actuales de equidad a menudo ajustan los modelos de decisión directamente, pasando por alto las intervenciones individuales.

Objetivo del estudio:

  • Introducir un nuevo marco para lograr la equidad a largo plazo a través del recurso algorítmico secuencial.
  • Desarrollar un enfoque basado en la causalidad para generar planes de recurso personalizados y temporalmente coherentes.

Principales métodos:

  • Se introdujo el recurso algorítmico causal secuencial para la equidad (SCARF).
  • Modelado causal estructural integrado con modelado generativo secuencial.
  • Restricciones de equidad incorporadas a corto y largo plazo con limitaciones presupuestarias.

Principales resultados:

  • SCARF genera trayectorias de recurso personalizadas que mitigan las disparidades en múltiples ciclos de decisión.
  • El análisis empírico reveló compensaciones entre la equidad a corto y largo plazo bajo intervenciones secuenciales.
  • Demostró la efectividad de SCARF en conjuntos de datos sintéticos y semisintéticos.

Conclusiones:

  • SCARF ofrece un marco práctico para analizar y mejorar la equidad a largo plazo en la toma de decisiones dinámicas.
  • El enfoque proporciona intervenciones viables para que los individuos aseguren resultados equitativos.
  • Destaca la importancia de considerar la dinámica temporal en las intervenciones de equidad.