Before revision:  Our contribution is an interdisciplinary review to inform how to measure, communicate, and use uncertainty as a form of transparency.
After revision:  Our contribution is an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, decision-making and fairness. We aim to encourage researchers and practitioners to measure, communicate, and use uncertainty as a form of transparency.