Mitigating Confirmation Bias Through Hand-Drawing Videos

Authors

Chenyu Lin (University of Wisconsin - Madison), Cindy Xiong Bearfield (Georgia Tech), Icy(Yunyi) Zhang (University of Wisconsin-Madison)

Presentation

Session

Can We Trust This Chart? (Asking for a Friend)

Time

Wednesday, Nov 11, 08:00 – 08:09 (US/Eastern) · session 08:00 – 09:30

Location

Hall America south

Keywords

Embodied cognition, bar graph, confirmation bias, hand-drawing

Abstract

Understanding data visualizations is essential for informed decision-making, yet interpretation is often shaped and even distorted by prior beliefs. We investigate whether an embodied pedagogical approach, in which viewers observe the dynamic hand-drawing of a visualization, can mitigate confirmation bias and improve interpretation accuracy. We conducted a study comparing static bar charts to videos in which charts are constructed through hand drawing, across contexts that either align with or challenge participants’ prior beliefs. The results indicate that hand-drawn videos helped participants accurately interpret data, even when the data conflicted with their prior beliefs. This approach also reduced belief-consistent errors and increased belief-overriding responses. These findings suggest that exposing the construction process of a visualization supports more accurate reasoning and mitigates the influence of confirmation bias. Consequently, this work introduces a promising design space for bias-mitigating data interfaces.

For Practitioners

This paper will be of interest to visualization designers, data journalists, educators, instructional designers, and practitioners who communicate data to broad audiences. These practitioners often need to present data on socially meaningful or belief-relevant topics, where viewers’ prior beliefs may shape how they interpret visual evidence. Practitioners can apply insights from this work by considering not only how visualizations are designed but also how they are presented. In particular, dynamic hand-drawing or step-by-step construction may help audiences attend to relationships among values, rather than relying only on salient visual features or prior beliefs. This approach may be useful in educational materials, public-facing data communication, and interactive data interfaces where accurate interpretation is especially important.