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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
Retraining with constraints should come after identifying the specific bias metrics to constrain.
- ✗
Remove the age feature from the model
Why it's wrong here
Removing age may not eliminate bias because correlated features can proxy for age, and it could reduce model performance.
- ✗
Collect more data for elderly patients to reduce representation bias
Why it's wrong here
Collecting more data may help but is not the first step; first measure the bias to understand its magnitude and cause.
- ✓
Use SageMaker Clarify to compute bias metrics and identify the extent of the disparity
Why this is correct
Measuring the bias is the prerequisite for any mitigation; Clarify provides metrics like difference in false negative rates.
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