Halbeegyada Caddaaladda AI: Wadannimada Dadkoo dhan ilaa Kediska Qalafsanaanta La Simo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Tusaalaha daynta ee hagaha, kooxda A waxa ay leedahay 40% doorashada halka kooxda B ay leedahay 20% qiimaha doorashada. Halbeegkee ayay tani jabinaysaa?
   • Equal opportunity
   • Demographic parity
   • Dejinta
   • Individual fairness
2. Fursada loo siman yahay ayaa u baahan intee le'eg tahay tirada kooxaha?
   • False positive rate
   • Selection rate
   • True positive rate
   • Positive predictive value
3. Maxay ku kordhinayaan fursadaha la siman yahay?
   • Equal false positive rates
   • Equal selection rates
   • Equal group sizes
   • Equal base rates
4. Tusaalaha, kooxda B heerka beenta ah ee togan waa 6 ee 80. Waa maxay sababta hooseeye 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. Maxay yihiin natiijooyinka suurtogalka ah ee ka yimid Choldechova iyo Kleinberg et al. dheh?
   • 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
   • Joogteynta joojinta
   • Falanqaynta qaybta ugu muhiimsan
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