AIF-C01 Guidelines for Responsible AI Practice Question
A healthcare organization uses an AI model to predict patient readmission risks. The model's predictions are used by doctors to allocate follow-up care. The organization wants to ensure compliance with responsible AI guidelines. Which practice best supports explainability?
⚠ Common exam trap
It's easy for candidates to confuse model performance metrics (accuracy) with explainability, or assume that automation and bias reduction are substitutes for interpretability, when in fact responsible AI requires transparent, human-understandable outputs.
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
✓
Providing feature importance scores for each prediction
Feature importance scores directly address explainability by showing which input factors (e.g., age, lab results) most influenced each prediction. This allows doctors to understand and trust the model's reasoning, which is a core requirement of responsible AI guidelines for high-stakes healthcare decisions.
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's overall accuracy exceeds 95%
Why it's wrong here
Overall accuracy is an aggregate performance metric; it says nothing about why an individual prediction was produced, so it cannot satisfy explainability. It is tempting because high accuracy suggests a trustworthy model, but it would be the correct choice when the requirement is predictive performance validation rather than interpretability.
- ✗
Using a black-box ensemble model that achieves highest accuracy
Why it's wrong here
A black-box ensemble obscures the feature contributions behind each prediction, so clinicians cannot obtain the reasoning needed for explainability. It is tempting because ensembles often maximise accuracy, but it would be the correct choice when raw predictive performance outweighs interpretability and no explanation obligation applies.
- ✗
Automating decisions without human review to reduce bias
Why it's wrong here
Automating decisions without human review removes the mechanism by which clinicians can interrogate and contest predictions, directly undermining explainability and accountability. It is tempting as a bias-reduction shortcut, but it would be correct only where low-risk, reversible decisions genuinely warrant full automation under governance.
- ✓
Providing feature importance scores for each prediction
Why this is correct
Feature importance scores reveal which input variables most influenced each individual readmission prediction, letting clinicians scrutinise the reasoning behind a recommendation. This directly satisfies the responsible AI explainability requirement by making the model's decision logic transparent to doctors allocating follow-up care.
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