215 citations · 309 across the 4 of their papers we have counts for
7 papers
Bayesian-Assisted Inference from Visualized Data
Yea-Seul Kim, Paula Kayongo, Madeleine Grunde-McLaughlin +1
A Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of informatio…
Visual Reasoning Strategies for Effect Size Judgments and Decisions
Alex Kale, Matthew Kay, Jessica Hullman
Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, user…
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
Sungsoo Ray Hong, Jessica Hullman, Enrico Bertini
As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-perf…
Why Authors Don't Visualize Uncertainty
Jessica Hullman
Clear presentation of uncertainty is an exception rather than rule in media articles, data-driven reports, and consumer applications, despite proposed techniques for communicating…
Illusion of Causality in Visualized Data
Cindy Xiong, Joel Shapiro, Jessica Hullman +1
Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program wou…
Decision-Making Under Uncertainty in Research Synthesis: Designing for the Garden of Forking Paths
Alex Kale, Matthew Kay, Jessica Hullman
To make evidence-based recommendations to decision-makers, researchers conducting systematic reviews and meta-analyses must navigate a garden of forking paths: a series of analytic…