AI Associate Ethical Considerations of AI Practice Question
A healthcare provider uses an AI system to predict patient readmission risk. The system was trained on historical data from the past five years, during which the hospital served a predominantly urban population. Recently, the hospital expanded to rural areas with different demographic and socioeconomic profiles. The AI predictions have been less accurate for rural patients, leading to misallocation of care resources. The AI Ethics committee is reviewing the system for potential bias. The model outputs a risk score from 0 to 100. The data science team has identified that the model uses features such as income, distance from hospital, and insurance type, which may correlate with race and socioeconomic status. The team wants to make the model fairer without retraining from scratch. Which approach best balances fairness and predictive accuracy?
⚠ Common exam trap
Salesforce often tests the misconception that removing sensitive features (like income or insurance type) is sufficient to eliminate bias, when in reality proxy variables and correlated features can still perpetuate discrimination, making post-processing or reweighing techniques more effective.
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
✓
Apply a post-processing calibration that adjusts the risk score thresholds separately for urban and rural populations to achieve equal false positive rates.
Post-processing calibration adjusts the decision thresholds for each subgroup (urban vs. rural) to equalize a fairness metric (e.g., false positive rate) without modifying the underlying model. This approach preserves the predictive signal from the original features while directly addressing the bias caused by distribution shift, making it the most practical solution when retraining from scratch is not feasible.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the features income, distance, and insurance type from the model and retrain.
Why it's wrong here
Removing features may reduce predictive accuracy and does not ensure fairness if other proxy features remain.
- ✗
Continue using the current model but add a disclaimer that predictions may be less accurate for rural patients.
Why it's wrong here
Adding a disclaimer does not mitigate the actual harm caused by biased predictions.
- ✓
Apply a post-processing calibration that adjusts the risk score thresholds separately for urban and rural populations to achieve equal false positive rates.
Why this is correct
Post-processing calibration can equalize error rates across groups without retraining, balancing fairness and accuracy.
- ✗
Retrain the model using only the latest year of data that includes rural patients.
Why it's wrong here
Retraining from scratch is time-consuming and may still have bias if the new data is not representative.
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