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La IA sin sesgos

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La IA sin sesgos

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Explora sesgos y justicia en IA

Created by

Lari Cervantes
Lari Cervantes
Mexico

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La IA sin sesgos
 

La IA sin sesgosOnline version

Explora sesgos y justicia en IA

by Lari Cervantes
1

¿Qué es el sesgo en IA?

2

¿Cuál es una fuente común de sesgo en IA?

3

¿Qué técnica ayuda a reducir sesgos en modelos?

4

¿Qué es la equidad en IA?

5

¿Qué implica la explicabilidad para reducir sesgos?

6

¿Qué rol juegan los datos demográficos en sesgos?

7

¿Qué método evalúa la equidad entre grupos?

8

¿Qué es un sesgo de muestra?

9

¿Cómo puede el sesgo afectar decisiones de IA en empleo?

10

¿Qué es una auditoría ética de IA?

Explicación

El sesgo surge cuando los datos o el diseño favorecen a ciertos grupos o resultados.

Datasets sesgados reflejan o amplifican desigualdades existentes.

Evaluaciones de sesgo y curación de datos mitigan sesgos.

La equidad busca no perjudicar a grupos vulnerables.

La transparencia ayuda a detectar sesgos y corregirlos.

Datos desbalanceados pueden inducir sesgos injustos.

GIA compara resultados entre grupos para identificar disparidades.

Si la muestra no representa, los resultados fallan en generalizar.

Sesgos en datos de candidatos pueden perpetuar discriminación.

Las auditorías evalúan impactos, seguridad y justicia.

 
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