Improving the Robustness of Data Visualization for Trustworthy Inference
Co-sponsored by the Goldman School of Public Policy
Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive visual data analysis, and data visualizations have become ubiquitous in communicative contexts like news and scientific publishing. However, despite these successes, our understanding of how to design robust visualizations for data-driven inference remains limited. For example, designing visualizations to maximize perceptual accuracy and users’ reported satisfaction can lead people to adopt visualizations that promote overconfident interpretations. Design philosophies that emphasize data exploration and hypothesis generation over other phases of analysis can encourage pattern-finding over sensitivity analysis and quantification of uncertainty. I will motivate alternative objectives for measuring the value of a visualization, and describe design approaches that better satisfy these objectives. I will discuss how the concept of a model check can help bridge traditionally exploratory and confirmatory activities, and suggest new directions for software and empirical research.
This lecture will also be live streamed via Zoom.
Jessica Hullman is Ginni Rometty Associate Professor of Computer Science at Northwestern University.
Her research addresses challenges that arise when people draw inductive inferences from data summaries. Her recent work contributes visualization techniques, applications, and evaluative frameworks for improving data-driven inference in visual data analysis, data communication, privacy budget setting, and responsive design.
Hullman’s work has been awarded best paper awards at IEEE VIS and ACM CHI and she has received a Microsoft Faculty award, a Google Faculty award, and NSF CAREER, Medium, and Small awards as PI, among others. She frequently speaks and blogs on topics related to visualization and reasoning about and representing uncertainty in data analysis and data-driven science.