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Experiencia interdisciplinaria para avanzar en la IA explicable centrada en el ser humano

Chloe R Bennett1, Heather Cole-Lewis2, Stephanie Farquhar3

  • 1Work done at Google via Pro Unlimited, Folsom, CA, USA; Northeastern University, Boston, MA, USA.

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Resumen

Este estudio presenta un modelo de inteligencia artificial (IA) centrado en el ser humano que utiliza la revisión de expertos interdisciplinarios para identificar y reducir el sesgo en la IA de atención médica. Este enfoque mejora el rendimiento de la IA y promueve la ciencia en equipo para futuras investigaciones.

Palabras clave:
Inteligencia artificialIA explicableIA centrada en el ser humanoSesgo en IAIA en atención médicaCiencia en equipo

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Área de la Ciencia:

  • Informática de la Salud
  • Inteligencia Artificial
  • Detección de Sesgos

Sus antecedentes:

  • La inteligencia artificial (IA) impacta significativamente la atención médica, sin embargo, el sesgo persistente y los problemas de rendimiento de subgrupos obstaculizan su máximo potencial.
  • La investigación existente enfatiza la representatividad de los datos y el rendimiento del modelo para reducir el sesgo y avanzar en la salud de la población.
  • La epidemiología social, la ciencia del comportamiento y la investigación de servicios de salud ofrecen marcos valiosos para interpretar los hallazgos de la IA, especialmente con la IA explicable (XAI).

Objetivo del estudio:

  • Presentar un novedoso modelo de circuito cerrado que integra la XAI generativa con un panel de expertos interdisciplinarios.
  • Evaluar críticamente las explicaciones del modelo de IA desde múltiples perspectivas para identificar sesgos y guiar la investigación futura.
  • Aprovechar la experiencia multidisciplinaria para mejorar el rigor de la IA y fomentar la ciencia en equipo.

Principales métodos:

  • Acoplamiento de la IA explicable generativa (XAI) con un proceso de revisión de panel de expertos interdisciplinarios.
  • Enfoque centrado en el ser humano para evaluar críticamente las explicaciones del modelo de IA.
  • Incorporación de información histórica y contextual a través de la interpretación de expertos.

Principales resultados:

  • El panel de expertos interdisciplinarios identificó eficazmente áreas de sesgo en los modelos de IA.
  • Las interpretaciones de expertos proporcionaron información informada histórica y contextual.
  • Se identificaron posibles factores de confusión y se integró evidencia científica de estudios previos para reducir el sesgo.

Conclusiones:

  • El marco centrado en el ser humano mejora el rigor metodológico para la mejora e implementación de modelos de IA en la atención médica.
  • Este enfoque fomenta de manera proactiva la ciencia en equipo y las oportunidades de investigación colaborativa.
  • El modelo ofrece una vía prometedora para mitigar el sesgos y mejorar el rendimiento de la IA en aplicaciones de salud.