Abstract:

Uncertain predictions permeate our daily lives (“will it rain today?”, “how long until my bus shows up?”, “who is most likely to win the next election?”). Fully understanding the uncertainty in such predictions would allow people to make better decisions, yet predictive systems usually communicate uncertainty poorly—or not at all. I will discuss ways to combine knowledge of visualization perception, uncertainty cognition, and task requirements to design visualizations that more effectively communicate uncertainty. I will also discuss ongoing work in systematically characterizing the space of uncertainty visualization designs and in developing ways to communicate (difficult- or impossible-to-quantify) uncertainty in the data analysis process itself. As we push more predictive systems into people’s everyday lives, we must consider carefully how to communicate uncertainty in ways that people can actually use to make informed decisions.

Bio:

Matthew Kay is an Assistant Professor in Computer Science and Communications Studies at Northwestern University working in human-computer interaction and information visualization. His research areas include uncertainty visualization, personal health informatics, and the design of human-centered tools for data analysis. He is intrigued by domains where complex information, like uncertainty, must be communicated to broad audiences, as in health risks, transit prediction, or weather forecasting. He co-directs the Midwest Uncertainty Collective (http://mucollective.co) and is the author of the tidybayes (https://mjskay.github.io/tidybayes/) and ggdist (https://mjskay.github.io/ggdist/) R packages for visualizing Bayesian model output and uncertainty.

Schedule – EST (UTC-5)

6:45 – 7:00: Networking (via Miro)

7:00 – 8:00: Presentation by Matthew Kay

8:00 – 8:30: Q & A