An AI model used for predicting hospital no-shows achieves 78% accu…

Question

An AI model used for predicting hospital no-shows achieves 78% accuracy overall, but the AI risk team discovers it has a false negative rate of 35% for elderly patients. What action is MOST appropriate?

Accepted Answer

Investigate whether the false negative rate disparity violates age discrimination regulations and assess whether model retraining with stratified sampling is required — Here's the deal — b is correct because a significantly higher false negative rate for elderly patients suggests systematic underperformance for this protected group, which may constitute age discrimination under applicable laws. The investigation should determine causation and whether retraining with stratified sampling can reduce the disparity.

Answer

Accept the model since overall accuracy meets the 75% organizational benchmark

Answer

Exclude elderly patients from the model's predictions and apply manual scheduling for that population

Answer

Increase the decision threshold for all patients to reduce the false negative rate across the board

An AI model used for predicting hospital no-shows achieves 78% accuracy overall, but the AI risk team discovers it has a false negative rate of 35% for elderly patients. What action is MOST appropriate?

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