Clarify

Amazon SageMaker Clarify integrates into SageMaker workflows to detect bias at two stages: pre-training (analyzing raw datasets for skewed label distributions or underrepresented groups) and post-training (measuring bias in a trained model’s predictions across demographic segments). It also generates feature-importance explanations using SHAP values, showing which input features most influenced a given prediction. A common exam confusion is treating bias detection and explainability as separate tools, but in Clarify both capabilities live in the same service. It produces human-readable reports supporting responsible-AI documentation, making it the go-to answer whenever a question asks how to audit a model for fairness or transparency on AWS.