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Understanding the predictions made by machine learning (ML) models and theirpotential biases remains a challenging and labor-intensive task that depends onthe application, the dataset, and the specific model. We present AmazonSageMaker Clarify, an explainability feature for Amazon SageMaker that launchedin December 2020, providing insights into data and ML models by identifyingbiases and explaining predictions. It is deeply integrated into AmazonSageMaker, a fully managed service that enables data scientists and developersto build, train, and deploy ML models at any scale. Clarify supports biasdetection and feature importance computation across the ML lifecycle, duringdata preparation, model evaluation, and post-deployment monitoring. We outlinethe desiderata derived from customer input, the modular architecture, and themethodology for bias and explanation computations. Further, we describe thetechnical challenges encountered and the tradeoffs we had to make. Forillustration, we discuss two customer use cases. We present our deploymentresults including qualitative customer feedback and a quantitative evaluation.Finally, we summarize lessons learned, and discuss best practices for thesuccessful adoption of fairness and explanation tools in practice.

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud