Cuestionario vinculado a una guía · duro Nivel

Métricas de equidad de IA: cuestionario sobre paridad demográfica y probabilidades igualadas

Prueba la paridad demográfica, la igualdad de oportunidades y las probabilidades igualadas con un ejemplo de préstamo trabajado, los resultados de imposibilidad y las herramientas Fairlearn y AIF360.

Rutas de guía relacionadasMétricas de equidad de IA explicadas
Pregunta 1 de 8

In the guide's loan example, group A has a 40% selection rate and group B has a 20% selection rate. Which metric does this violate?

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  1. In the guide's loan example, group A has a 40% selection rate and group B has a 20% selection rate. Which metric does this violate?
    • Equal opportunity
    • Demographic parity
    • Calibración
    • Individual fairness
  2. Equal opportunity requires which quantity to be equal across groups?
    • False positive rate
    • Selection rate
    • True positive rate
    • Positive predictive value
  3. What does equalized odds add on top of equal opportunity?
    • Equal false positive rates
    • Equal selection rates
    • Equal group sizes
    • Equal base rates
  4. In the example, group B's false positive rate is 6 of 80. Why is the denominator 80?
    • It is the number of approved applicants
    • It is the total population of both groups
    • It is 100 minus the 20 approved applicants
    • It is the number of group B applicants who would not repay
  5. What do the impossibility results from Chouldechova and Kleinberg et al. say?
    • No model can be accurate for more than one group
    • When base rates differ, an imperfect classifier cannot satisfy predictive parity or calibration together with equal false positive and false negative rates
    • Demographic parity always implies equalized odds
    • Fairness metrics apply only to linear models
  6. Why does removing the protected attribute from the inputs not guarantee fairness?
    • Models always add the attribute back automatically
    • Laws require the attribute to be included
    • Correlated proxies such as zip code can carry the same information
    • Removing features always lowers accuracy to zero
  7. Which technique chooses group-specific thresholds, sometimes randomized, to equalize true and false positive rates?
    • Hardt et al.'s post-processing, available as Fairlearn's ThresholdOptimizer
    • AIF360 Reweighing
    • Regularización del abandono
    • Análisis de componentes principales
  8. Why should fairness metrics be reported with confidence intervals and for intersectional subgroups?
    • Because the four-fifths rule requires exactly three decimal places
    • Because intersectional groups always have identical rates
    • Because Fairlearn cannot calculate point estimates
    • Because small groups make rate estimates noisy, and a model can look fair on single attributes while failing a combination of them