New Paper on Selection-Bias Corrected Visualizations at IEEE VIS 2020
Borland et al. introduce new methods for selection-bias-corrected visualizations.
The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threatens the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process.
In the first of two VACLab papers that will be presented at IEEE VIS 2020. Four VACLab teammates–David Borland, Jonathan Zhang, Smiti Kaul, and David Gotz–collaborated on a paper titled Selection-Bias-Corrected Visualization via Dynamic Reweighting. The paper introduces Dynamic reweighting (DR), a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. The paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.
The paper will be presented during the IEEE VIS Conference in October and will appear in print in the January 2021 issue of IEEE Transactions on Visualization and Computer Graphics. An open access pre-print has been posted to arXiv and an open-access video figure is available via vimeo.
More details can be found on the article’s page on this website.
Reference
David Borland, Jonathan Zhang, Smiti Kaul, David Gotz (2021). Selection-Bias-Corrected Visualization via Dynamic Reweighting. IEEE Transactions on Visualization and Computer Graphics (Volume 27, Issue 1).