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In this paper, we describe an open source Python toolkit named UncertaintyQuantification 360 (UQ360) for the uncertainty quantification of AI models. Thegoal of this toolkit is twofold: first, to provide a broad range ofcapabilities to streamline as well as foster the common practices ofquantifying, evaluating, improving, and communicating uncertainty in the AIapplication development lifecycle; second, to encourage further exploration ofUQ's connections to other pillars of trustworthy AI such as fairness andtransparency through the dissemination of latest research and educationmaterials. Beyond the Python package (\url{https://github.com/IBM/UQ360}), wehave developed an interactive experience (\url{http://uq360.mybluemix.net}) andguidance materials as educational tools to aid researchers and developers inproducing and communicating high-quality uncertainties in an effective manner.

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