A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements. Which solution will meet these requirements?
A.
Configure the security and compliance by using Amazon Inspector.
B.
Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
C.
Encrypt and secure training data by using Amazon Macie.
D.
Gather more data. Use Amazon Rekognition to add custom labels to the data.
Amazon SageMaker Clarify is specifically designed to help make machine learning models more transparent and explainable by generating metrics and reports on model bias, data bias, and feature importance.
Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify: Amazon SageMaker Clarify helps provide transparency and explainability to machine learning models by generating metrics, reports, and visual explanations of the model’s predictions. This is crucial for meeting regulatory requirements in domains like healthcare, where understanding how a model arrives at its decisions is essential for validation and trust. SageMaker Clarify also helps identify potential biases in the model, which is important for ensuring fair and responsible use of AI.
B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
Amazon SageMaker Clarify helps in identifying bias and explaining predictions made by machine learning models, which aligns well with the need for transparency and explainability to meet regulatory requirements.
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