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Autor:
Michele Donini, Jason Gelman, Ankit Siva, Satish Gollaprolu, Michaela Hardt, Scott Rees, Keerthan Vasist, ErhYuan Tsai, Kevin Haas, Nick McCarthy, Xinyu Liu, Tyler Hill, Xiaoguang Chen, John He, Sanjiv Ranjan Das, Xiaoyi Cheng, Muhammad Bilal Zafar, Pedro Larroy, Ashish Rathi, Krishnaram Kenthapadi, Pinar Yilmaz
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1728d15bde7bafc389e8a182ecf5eb82