AI0-001 AI Governance and Ethics Practice Question
A healthcare AI system diagnosing diabetic retinopathy from retinal images shows high accuracy overall but significantly lower recall for patients with darker skin tones. Which fairness metric would BEST capture this disparity by comparing true positive rates across groups?
⚠ Common exam trap
AI0-001 often tests the confusion between different fairness metrics. Candidates may choose demographic parity because it is commonly mentioned, but it does not address TPR disparities. The key is to recognize that the question asks for a metric comparing true positive rates across groups, which is equalised odds.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Equalised odds
Equalised odds is a fairness metric that requires both true positive rates (TPR) and false positive rates (FPR) to be equal across groups. In this scenario, the disparity is specifically in recall (TPR) for patients with darker skin tones, so equalised odds directly captures the difference in TPR between groups. It ensures that the model's ability to correctly identify positive cases is independent of the group attribute.
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 compares predicted probabilities against observed outcomes within each group, not true positive rates between groups. The stem describes a recall disparity, so calibration cannot expose it. Calibration is correct when probability estimates must be reliable per group, such as risk scoring where predicted likelihoods drive clinical decisions.
- ✗
Demographic parity
Why it's wrong here
Demographic parity equalises positive prediction rates across groups, ignoring whether predictions match actual outcomes. The stem's lower recall for darker skin tones is a true positive rate gap, which equalised opportunity measures directly. Demographic parity would be the right choice when selection rates must match regardless of ground-truth prevalence.
- ✓
Equalised odds
Why this is correct
Equalised odds compares true positive rates and false positive rates across groups, so it directly exposes the lower recall for darker-skinned patients. Demographic parity or equal opportunity alone would not capture the full disparity across both error types that this metric requires.
- ✗
Individual fairness
Why it's wrong here
Individual fairness requires similar treatment for similar individuals, comparing like-for-like cases rather than group-level true positive rates. The stem's disparity is between skin-tone groups, so a group metric is needed. Individual fairness suits scenarios demanding consistency between comparable individuals, not measuring recall gaps across protected groups.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.