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



Michaela Hardt , Xiaoguang Chen , Xiaoyi Cheng , Michele Donini , Jason Gelman , Satish Gollaprolu , John He , Pedro Larroy , Xinyu Liu , Nick McCarthy , Ashish Rathi , Scott Rees , Ankit Siva , ErhYuan Tsai , Keerthan Vasist , Pinar Yilmaz , Muhammad Bilal Zafar , Sanjiv Das , Kevin Haas , Tyler Hill , Krishnaram Kenthapadi

* In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2974-2983 (2021) 

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Linear Dynamics: Clustering without identification



Chloe Ching-Yun Hsu , Michaela Hardt , Moritz Hardt


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Explaining an increase in predicted risk for clinical alerts



Michaela Hardt , Alvin Rajkomar , Gerardo Flores , Andrew Dai , Michael Howell , Greg Corrado , Claire Cui , Moritz Hardt


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Scalable and accurate deep learning for electronic health records



Alvin Rajkomar , Eyal Oren , Kai Chen , Andrew M. Dai , Nissan Hajaj , Peter J. Liu , Xiaobing Liu , Mimi Sun , Patrik Sundberg , Hector Yee , Kun Zhang , Gavin E. Duggan , Gerardo Flores , Michaela Hardt , Jamie Irvine , Quoc Le , Kurt Litsch , Jake Marcus , Alexander Mossin , Justin Tansuwan , De Wang , James Wexler , Jimbo Wilson , Dana Ludwig , Samuel L. Volchenboum , Katherine Chou , Michael Pearson , Srinivasan Madabushi , Nigam H. Shah , Atul J. Butte , Michael Howell , Claire Cui , Greg Corrado , Jeff Dean

* npj Digital Medicine 1:18 (2018) 
* Published version from https://www.nature.com/articles/s41746-018-0029-1 

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