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Froggy Jumps
Froggy Jumps

Detectando sesgos (Caso a caso)

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Detectando sesgos (Caso a caso)

Froggy Jumps

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About this activity

Identifica la fuente de sesgo en cada situación

Created by

Academia JuventudesGTO
Academia JuventudesGTO
Mexico

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Detectando sesgos (Caso a caso)
 

Froggy Jumps

Detectando sesgos (Caso a caso)Online version

Identifica la fuente de sesgo en cada situación

by Academia JuventudesGTO
1

Una IA médica fue entrenada principalmente con información de pacientes de una sola región del mundo.

2

Un equipo desarrollador decide que la experiencia laboral debe tener mucho más peso que otros factores al evaluar candidaturas.

3

Las personas encargadas de clasificar imágenes etiquetan incorrectamente una parte importante de los datos de entrenamiento.

4

Un sistema de recomendación aprende a partir de información histórica donde ciertos grupos tenían poca representación.

5

Una universidad desarrolla una IA para identificar estudiantes con "alto potencial" utilizando criterios definidos únicamente por un pequeño grupo de especialistas.

6

Una empresa utiliza registros históricos de contratación donde predominaban perfiles muy similares entre sí.

 
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