  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 such as fairness and
transparency through the dissemination of latest research and education
materials. Beyond the Python package (\url{https://github.com/IBM/UQ360}), we
have developed an interactive experience (\url{http://uq360.mybluemix.net}) and
guidance materials as educational tools to aid researchers and developers in
producing and communicating high-quality uncertainties in an effective manner.
