AI0-001 AI Security, Ethics and Governance Practice Question
A hospital deploys an AI diagnostic assistant that analyzes medical images. The system has been in use for six months, and radiologists have reported that the AI is increasingly confident in its predictions, but sometimes misses rare conditions. The AI ethics board is concerned about overreliance and potential harm from false negatives. They want to implement a governance framework that ensures appropriate human oversight. The hospital has a limited IT budget. What is the best approach?
⚠ Common exam trap
CompTIA AI often tests the distinction between passive warnings (like option B) and active workflow controls (like option A), where candidates mistakenly believe that a simple disclaimer is sufficient for governance when actual process enforcement is required.
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
✓
Implement a human-in-the-loop process where the AI flags low-confidence or rare condition predictions for mandatory radiologist review
A human-in-the-loop process that triggers mandatory radiologist review only for low-confidence or rare-condition predictions directly addresses the risk of overreliance and false negatives without overwhelming the limited IT budget. This targeted oversight ensures that the AI's increasing confidence does not lead to missed rare conditions, while still allowing routine high-confidence predictions to proceed efficiently. The approach balances safety and resource constraints by focusing human attention where the AI is most likely to err.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a human-in-the-loop process where the AI flags low-confidence or rare condition predictions for mandatory radiologist review
Why this is correct
Routing low-confidence and rare-condition predictions to mandatory radiologist review directly counters false negatives and overreliance, satisfying the ethics board's oversight requirement while respecting the limited budget, since it adds a triage workflow rather than expensive new infrastructure.
- ✗
Add a warning to the AI interface that says 'This tool may miss rare conditions'
Why it's wrong here
A warning label shifts responsibility onto the reader without changing how predictions are verified, so it provides no enforceable oversight mechanism. It is tempting because it is cheap and raises awareness, and it would be correct as a supplementary transparency notice alongside a substantive review process.
- ✗
Require all AI predictions to be reviewed by a radiologist before final diagnosis
Why it's wrong here
Mandating radiologist review of every prediction imposes a blanket human checkpoint that consumes scarce radiology time, conflicting with the limited budget while not targeting the rare conditions the AI misses. It is tempting because universal review guarantees oversight, and it would be correct where volume is low and staffing ample.
- ✗
Increase the AI's false positive threshold to reduce missed cases
Why it's wrong here
Raising the false positive threshold changes the model's operating point, trading more false alarms for fewer missed cases, which does not create human oversight. It is tempting because it directly targets false negatives, and it would be correct if the governance aim were purely statistical sensitivity tuning.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.