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

@article{Hardt2021AmazonSC,
  title={Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud},
  author={Michaela Hardt and Xiaoguang Chen and Xiaoyi Cheng and Michele Donini and Jason Gelman and Satish Gollaprolu and John He and Pedro Larroy and Xinyu Liu and Nick McCarthy and Ashish M. Rathi and Scott Rees and Ankit Siva and ErhYuan Tsai and Keerthan Vasist and Pinar Yilmaz and Muhammad Bilal Zafar and Sanjiv Das and Kevin Haas and Tyler Hill and Krishnaram Kenthapadi},
  journal={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  year={2021}
}
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… 

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