AI0-001 AI Governance and Ethics Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Calibration

  Why it's wrong here

  Calibration matches predicted probabilities to observed outcome rates within each group; it constrains score reliability, not error rates across groups. Calibration is the right target when probability estimates must be trustworthy, such as risk scoring, rather than equalising true positive rates.

• ✗

  Individual fairness

  Why it's wrong here

  Individual fairness requires similar predictions for similar individuals, treating each person consistently without aggregating by group. It fits cases where like candidates should receive like scores, but it does not measure or equalise true positive rates between demographic groups.

• ✗

  Demographic parity

  Why it's wrong here

  Demographic parity equalises positive prediction rates across groups regardless of actual outcomes, so it ignores true positives and false negatives entirely. It suits contexts demanding equal selection rates, not the equal opportunity requirement of matching true positive rates.

• ✓

  Equalized odds

  Why this is correct

  Equalized odds requires equal true positive rates and false positive rates across demographic groups, so it directly matches the stated TPR parity goal. Demographic parity or equal opportunity would not capture both error rates simultaneously.