A healthcare provider trains a machine-learning model to predict patient readmission risk. The compliance team demands transparency into how each prediction was produced. Which benefit of Google Cloud's explainable AI directly addresses this need?
It provisions additional Cloud TPUs on demand to reduce model training time.
It automatically tunes hyperparameters to maximize the model's overall accuracy.
It reveals the contribution of each input feature to every prediction, supporting regulatory transparency.
It ensures all training data is encrypted by default in BigQuery tables.
Explainable AI provides feature-attribution information, showing which input features most influenced an individual prediction. This transparency helps regulated industries satisfy audit and compliance requirements. Hyperparameter tuning, encryption at rest, and on-demand TPU scaling are valuable capabilities, but they do not explain how the model arrived at a specific result and therefore do not meet the compliance team's request.
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