Deep learning methods have been a tremendously effective approach to problems in computer vision and natural language processing. However, these black-box models can be difficult to deploy in practice as they are known to make unpredictable mistakes that can be hard to analyze and correct. In this talk, I will present collaborative research to develop visually interactive interfaces for probabilistic deep learning models, with the goal of allowing users to examine and correct black-box models through visualizations and interactive inputs. Through co-design of models and visual interfaces we will take the necessary next steps for model interpretability. Achieving this aim requires active investigation into developing new deep learning models and analysis techniques, and integrating them within interactive visualization frameworks.