In this paper, we synthesize a design space for belief-driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main de- sign considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief-driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
Check out the paper from EuroVIS 2022.
In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief dis- tractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants’ reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual’s belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one’s belief system.
Check out the paper from EuroVIS 2023.
User interaction patterns are a powerful tool for making inferences about the person performing those interactions. In this paper, we introduce a set of metrics to statistically characterize those interactions according to how much they deviate from an "unbiased" distribution of interactions with the data using concepts of coverage and distribution. These metrics can be applied to real-time interactions with an interface to capture potential biases over time.
Check out the paper from IEEE VIS 2017.
We demonstrate that the Dunning-Kruger Effect exists in a spatial reasoning task: the 15-puzzle game. Furthermore, we demonstrate that the effect leads to different patterns in user interactive behavior and has correlations with personality characteristics.
Check out the paper from the TREX Workshop at IEEE VIS 2022.
Designing Bias Mitigation Interventions
We derive a design space comprised of 8 dimensions that can be manipulated to impact a user’s cognitive and analytic processes and describe them through an example hiring scenario. This design space can be used to guide and inform future vis systems that may integrate cognitive processes more closely.
Check out the paper from IEEE VIS 2019.
We implemented one intervention, interaction traces, in the Lumos system and conducted a series of experiments to test the effectiveness.
Check out the Lumos paper from IEEE VIS 2021.
Check out the Left, Right, and Gender paper from IEEE VIS 2021.
Transformer-Based Interactive Literature Review
We developed an interactive table-based visualization for searching academic literature using a transformer-based approach. The system, VitaLITy, has an initial set of 59k articles from popular visualization venues. The open-source code can be augmented to search literature from other academic sources as well. An alternative to keyword searches, find semantically similar documents by providing a set of initial seed papers or providing a working paper title and abstract to kickstart the literature review of a new project idea.
Check out the paper from IEEE VIS 2021.
Try it out on the web.