Before revision:  The machine learning (ML) community has mostly considered explainabilityas a proxy for transparency. With this work, we seek to encourage researchers to study uncertainty as a form of transparency and practitioners to communicate uncertainty estimates to stakeholders . First, we discuss methods for assessing uncertainty.
After revision:  Until now, most research into algorithmic transparency has predominantly focused on explainability. Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders. However, understanding a model's specific behavior alone might not be enough for stakeholders to gauge whether the model is wrong or lacks sufficient knowledge to solve the task at hand. In this paper, we argue for considering a complementary form of transparency and practitioners to communicate uncertainty estimates to stakeholders . First, we discuss methods for assessing uncertainty.