  Understanding the predictions made by machine learning (ML) models and their
potential biases remains a challenging and labor-intensive task that depends on
the application, the dataset, and the specific model. We present Amazon
SageMaker Clarify, an explainability feature for Amazon SageMaker that launched
in December 2020, providing insights into data and ML models by identifying
biases and explaining predictions. It is deeply integrated into Amazon
SageMaker, a fully managed service that enables data scientists and developers
to build, train, and deploy ML models at any scale. Clarify supports bias
detection and feature importance computation across the ML lifecycle, during
data preparation, model evaluation, and post-deployment monitoring. We outline
the desiderata derived from customer input, the modular architecture, and the
methodology for bias and explanation computations. Further, we describe the
technical challenges encountered and the tradeoffs we had to make. For
illustration, we discuss two customer use cases. We present our deployment
results including qualitative customer feedback and a quantitative evaluation.
Finally, we summarize lessons learned, and discuss best practices for the
successful adoption of fairness and explanation tools in practice.
