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.

• ✓

  Demographic parity

  Why this is correct

  Demographic parity compares the positive prediction rate between groups, directly quantifying whether approval rates are equal regardless of protected attributes. This matches the requirement to measure equal positive prediction rates across groups, exposing the historical denial disparity.

• ✗

  Calibration

  Why it's wrong here

  Calibration ensures predicted probabilities match observed outcome frequencies within groups; it does not compare positive prediction rates. It is tempting because it is a group-aware fairness measure, and would be correct when the requirement is that predicted risk scores are equally reliable across groups, not equal approval rates.

• ✗

  Individual fairness

  Why it's wrong here

  Individual fairness requires similar predictions for similar individuals, not equal positive prediction rates across groups. It is tempting because it addresses discrimination at the person level, and would be correct when the requirement is consistency between comparable individuals rather than group-level parity of approval rates.

• ✗

  Equalised odds

  Why it's wrong here

  Equalised odds requires equal true positive and false positive rates across groups, not equal positive prediction rates. It is tempting because it is a group fairness metric, and would be correct when the requirement is parity of error rates conditional on the actual outcome, rather than demographic parity of approvals.