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AIF-C01 Practice Question: A healthcare organization uses an ML model to…
A healthcare organization uses an ML model to predict patient readmission risk. The model performs well overall but has significantly higher false negative rates for elderly patients. The team needs to mitigate this bias. Which step should they take FIRST?
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
✓
Use SageMaker Clarify to compute bias metrics and identify the extent of the disparity
The first step is to identify and understand the bias through measurement. SageMaker Clarify can compute fairness metrics like false negative rate difference to quantify the disparity. Retraining with fairness constraints or reweighting are mitigation steps that come after measurement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the model with fairness constraints using SageMaker Clarify
Why it's wrong here
Fairness constraints presuppose a quantified disparity and a chosen fairness metric; applying them before measuring the false-negative gap risks optimising the wrong objective. SageMaker Clarify is the correct tool for that measurement step, which must precede constrained retraining in this scenario.
- ✗
Remove the age feature from the model
Why it's wrong here
Dropping age removes an explicit feature but leaves proxy variables such as diagnosis codes and prior admissions, so the disparity in false negatives persists while auditability is lost. Feature removal suits scenarios where a protected attribute is illegally or unnecessarily used, not where measured outcome disparity requires mitigation.
- ✗
Collect more data for elderly patients to reduce representation bias
Why it's wrong here
Collecting more elderly data addresses representation bias, but the stem describes a performance gap in false negatives, not underrepresentation. The first step is measuring and diagnosing the disparity, for example with SageMaker Clarify bias metrics, before choosing a mitigation such as resampling or reweighting.
- ✓
Use SageMaker Clarify to compute bias metrics and identify the extent of the disparity
Why this is correct
SageMaker Clarify quantifies bias through metrics such as disparate impact and conditional demographic disparity across the elderly subgroup, establishing the baseline magnitude before any mitigation. Measuring the disparity first satisfies the stem's requirement to identify the extent of bias, since remediation techniques cannot be selected or validated without that evidence.
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