Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Harmanpreet Kaur
Harmanpreet Kaur
Harsha Nori
Harsha Nori
Samuel Jenkins
Samuel Jenkins
Rich Caruana
Rich Caruana
Jennifer Wortman Vaughan
Jennifer Wortman Vaughan

CHI '20: CHI Conference on Human Factors in Computing Systems Honolulu HI USA April, 2020, pp. 1-14, 2020.

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Abstract:

Machine learning (ML) models are now routinely deployed in domains ranging from criminal justice to healthcare. With this newfound ubiquity, ML has moved beyond academia and grown into an engineering discipline. To that end, interpretability tools have been designed to help data scientists and machine learning practitioners better underst...More

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