The correct choice is SageMaker Clarify. This service is designed to compute dataset and prediction bias metrics and to provide explainability that helps detect and mitigate unfair outcomes and reduce legal and compliance risk.

SageMaker Clarify can measure disparities across demographic groups and produce fairness metrics and explanations that show which features influence predictions. Teams can use the metrics and explanations to compare groups, identify biased behavior, and apply preprocessing or postprocessing mitigation techniques to improve fairness.

Model Cards focus on documenting model lineage, intended use, and evaluation metrics for governance and transparency, and they do not perform statistical bias analysis or mitigation by themselves.

Guardrails for Bedrock provides content safety and output filtering for foundation model interactions, and it is not a substitute for the statistical fairness testing and explainability that Clarify provides.

Amazon Comprehend offers managed NLP features such as sentiment analysis and entity recognition, and it does not provide the bias measurement and mitigation tooling needed to evaluate model fairness across demographic groups.

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