Designs to Support Trustworthy Visual Data Communication
Abstract: Well-chosen data visualizations can lead to powerful and intuitive processing by a viewer, both for visual analytics and data storytelling. When badly chosen, visualizations leave important patterns opaque or misunderstood. So how can we design an effective visualization? I will share several empirical studies demonstrating that visualization design can influence viewer perception, interpretation, and trust in data, drawing on methods and insights from cognitive psychology. I leverage these findings to design natural language interfaces that recommend effective visualizations to answer user queries and help users extract meaningful messages from data.
I then identify three challenges in developing such interfaces. First, visualizations often present complex patterns that require effortful reasoning, and this complexity can both trigger critical thinking and shape how viewers calibrate trust. This makes it essential to understand how people perceive, reason about, and sometimes misinterpret data. Second, natural language queries describing takeaways from visualizations can be ambiguous and difficult to model, requiring deeper insight into how people articulate intended messages. Third, in this era of pervasive automation, we face a profound crisis of losing human agency. I position data visualization as a bridge between human cognition and machine intelligence and discuss ways to design visualizations that provoke critical thinking and calibrate trust.
Bio: Cindy Xiong Bearfield is an Assistant Professor in the School of Interactive Computing at Georgia Tech. Her research bridges psychology and data visualization to understand how people perceive, reason with, and make decisions from visual data. Through empirical studies of human cognition, she designs visualizations and AI-supported tools that help people build calibrated trust in complex information, supporting clearer analysis, compelling storytelling, and thoughtful engagement with AI.
Cindy earned her PhD in Cognitive Psychology and an MS in Statistics from Northwestern University. Her work has been recognized with an NSF CAREER Award, a Google Research Scholar Award, a Dolby ATG Research Award, and best-paper honors at leading HCI and visualization venues, including ACM CHI and IEEE VIS.
