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Quiz COMPAS i błąd w algorytmach recydywy

Testuje ustalenia ProPublica dotyczące współczynników błędów COMPAS, obrony przed kalibracją firmy Northpointe i tego, dlaczego definicje uczciwości są sprzeczne, gdy różnią się stawki podstawowe.

Powiązane ścieżki prowadząceBłąd algorytmu recydywy Compasa
Pytanie 1 z 8

Among defendants who did NOT reoffend, what did ProPublica find about Black defendants compared with white defendants?

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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.

  1. Among defendants who did NOT reoffend, what did ProPublica find about Black defendants compared with white defendants?
    • They were less likely to be scored at all
    • They had identical risk labels
    • They were nearly twice as likely to be labeled higher risk
    • They were more likely to be labeled lower risk
  2. What was the core of Northpointe's defense of COMPAS?
    • At each score level, Black and white defendants reoffended at similar rates (calibration)
    • The tool used race to correct for historical bias
    • ProPublica had analyzed the wrong county
    • The tool was never used in any court
  3. Under what condition does the impossibility result say calibration and equal error rates cannot all hold?
    • When the model uses more than 100 features
    • When the dataset has fewer than 10,000 people
    • When race is included as an input
    • When groups have different base rates and the predictor is not perfect
  4. What did the Wisconsin Supreme Court hold in State v. Loomis?
    • COMPAS is unconstitutional and banned from courts
    • Judges may consider COMPAS, but it cannot be the deciding factor, and written cautions are required
    • COMPAS scores must decide the length of sentences
    • Defendants must be shown the full source code
  5. Is race an input to COMPAS?
    • Yes, it is the most heavily weighted factor
    • Yes, but only for defendants in Florida
    • No; it uses criminal history and questionnaire answers
    • No; it uses only a defendant's age
  6. What did the 2018 study by Julia Dressel and Hany Farid find?
    • Untrained online volunteers predicted rearrest about as accurately as COMPAS
    • COMPAS was more than 90 percent accurate
    • Judges ignored COMPAS scores entirely
    • COMPAS had no racial disparities in Broward County
  7. Using FPR = (p/(1-p)) x ((1-PPV)/PPV) x (1-FNR), if PPV and FNR are equal across groups but group A has a higher base rate p, what happens to group A's false positive rate?
    • It must be lower
    • It must be zero
    • It is unaffected by p
    • It must be higher
  8. Why does leaving race out of a risk model not guarantee unbiased outcomes?
    • Courts require race to be included
    • Correlated inputs such as prior arrests can carry historical policing patterns
    • Models cannot run without demographic data
    • Leaving race out always makes the model less accurate for every group