Visualization facilitates data communication across the sciences and society at large, yet it can be hard to know whether a visualization gives an honest depiction of evidence. One reason for this is that visualizations provide necessarily incomplete views on complex datasets. Charts that attempt to convey too much information become incomprehensible, so honest and effective visualization design requires authors to choose what information to disclose and, conversely, what aspects of data will be hidden or distorted. This project will address the problem of responsible data disclosure through visualizations. For visualization authors, it will build tools that help them balance goals such as effective communication and protecting the privacy of data subjects. For audiences, it will develop new ways to support skepticism about what a chart cannot show by design. The project will also produce novel educational materials and games to help students learn to use visualizations responsibly and avoid misinterpretations. Together, these activities will create a practical framework for understanding and espousing ethical standards for data communication. This project reframes visualization as a mechanism for data disclosure. It develops a theory defining visualization design goals in terms of balancing forms of information loss that designers and audiences care about. The theory makes these losses computable by grounding them in mathematical formalisms developed through analysis of examples, synthesis, and expert interviews. Codifying this formalism in software will enable automated reasoning over the space of possible visualization designs suited to a given goal. Indexing this design space on relevant forms of information loss will enable new ways to recommend solutions for visualization authors, as well as new interfaces that generate assistive explanations for audiences. These tools will be evaluated through software testing, user studies, and controlled experiments. To