• Corpus ID: 235313948

# Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

@article{Ghosh2021UncertaintyQ3,
title={Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI},
author={Soumya Shubhra Ghosh and Qingzi Vera Liao and Karthikeyan Natesan Ramamurthy and Jir{\'i} Navr{\'a}til and Prasanna Sattigeri and Kush R. Varshney and Yunfeng Zhang},
journal={ArXiv},
year={2021},
volume={abs/2106.01410}
}
• Published 2 June 2021
• Computer Science
• ArXiv
In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models. The goal of this toolkit is twofold: first, to provide a broad range of capabilities to streamline as well as foster the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle; second, to encourage further exploration of UQ’s connections to other pillars of trustworthy AI…

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