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Sesgo Algorítmico: Verdadero o Falso

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Sesgo Algorítmico: Verdadero o Falso

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Juego corto sobre sesgo algorítmico.

Created by

Paula Quiroz Mendoza
Paula Quiroz Mendoza
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Sesgo Algorítmico: Verdadero o Falso
 

Sesgo Algorítmico: Verdadero o FalsoOnline version

Juego corto sobre sesgo algorítmico.

by Paula Quiroz Mendoza
1

La diversidad de datos de entrenamiento ayuda a reducir sesgos.

2

Los sesgos pueden ser introducidos por la selección de características relevantes.

3

Un modelo con alto rendimiento en precisión nunca presenta sesgo.

4

Al aumentar el tamaño de los datos, el sesgo desaparece por completo.

5

La normalización de datos elimina cualquier sesgo sin necesidad de revisiones.

6

El sesgo algorítmico solo afecta a grupos minoritarios y no a la población en general.

7

El sesgo algorítmico ocurre cuando los datos de entrenamiento reflejan prejuicios históricos.

8

Si el usuario no denuncia un problema, no hay sesgo en el sistema.

9

Los modelos pueden ser auditados para identificar y mitigar sesgos.

10

Sesgos de datos pueden llevar a decisiones injustas en contratación o crédito.

 
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