AI0-001 AI Implementation and Operations Practice Question
An AI system is being implemented in a healthcare setting. Which TWO ethical considerations should be prioritized?
⚠ Common exam trap
CompTIA often tests the distinction between ethical priorities and operational or financial goals, tricking candidates into selecting cost-saving or efficiency options instead of fairness and explainability.
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
✓
Ensuring the model does not exhibit racial or gender bias
Option A is correct because in a healthcare AI system, ensuring the model does not exhibit racial or gender bias is a core ethical requirement: biased training data or features can produce discriminatory diagnostic or treatment recommendations that harm protected patient groups, violating fairness and equity principles in clinical care. Option C is correct because providing explainable predictions to doctors supports transparency, accountability, and informed clinical decision-making; clinicians must understand the basis of AI recommendations to validate them, obtain patient consent, and meet medical-legal and regulatory obligations. Option B is not an ethical consideration but a financial/business objective, and cost reduction does not justify compromising patient welfare. Option D is not appropriate because replacing human judgment entirely with AI removes clinician oversight and accountability, which is ethically and legally unacceptable in healthcare. Option E is also a cost/licensing concern rather than an ethical priority, and using open-source models does not by itself address fairness, transparency, or patient safety.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensuring the model does not exhibit racial or gender bias
Why this is correct
Bias mitigation directly addresses healthcare's duty of non-maleficence and equity: a model trained on skewed historical data can systematically underdiagnose protected groups. Auditing and correcting for racial or gender bias satisfies the ethical requirement that diagnostic benefit be distributed fairly across patient populations, preventing discriminatory clinical outcomes.
- ✗
Maximizing cost reduction for the hospital
Why it's wrong here
Cost reduction is a financial outcome, not an ethical principle, and optimising it can conflict with patient welfare and equitable access. It is tempting because budget constraints genuinely shape healthcare AI procurement, so cost appears in business cases — but the question asks for ethical considerations such as beneficence and fairness.
- ✓
Providing explainable predictions to doctors
Why this is correct
Explainability satisfies clinicians' need to understand and validate each prediction before acting on it. In healthcare, opaque outputs undermine informed consent and accountability; providing feature attributions or reasoning lets doctors detect spurious correlations, override unsafe recommendations, and retain professional responsibility for diagnosis, which regulatory and ethical frameworks demand.
- ✗
Replacing human judgment entirely with AI
Why it's wrong here
Full replacement removes clinician oversight, eliminating accountability and the human review that safety-critical healthcare decisions require. It is tempting because automation promises consistency and reduced workload, and partial AI assistance is legitimate — but complete substitution of human judgment violates oversight and liability requirements.
- ✗
Using open-source models to reduce licensing costs
Why it's wrong here
Licensing cost is a procurement matter, not an ethical consideration, and open-source licences say nothing about clinical validity or bias. It is tempting because open-source models genuinely lower barriers to entry and enable transparency auditing, which supports ethics — but cost reduction itself is not the ethical priority here.
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