Conveying Uncertainty in Data Visualizations to Screen-Reader Users Through Non-Visual Means
Ather Sharif, Ruican Zhong, Yadi Wang
Abstract
Incorporating uncertainty in data visualizations is critical for users to interpret and reliably draw informed conclusions from the underlying data. However, visualization creators conventionally convey the information regarding uncertainty in data visualizations using visual techniques (e.g., error bars), which disenfranchises screen-reader users, who may be blind or have low vision. In this preliminary exploration, we investigated ways to convey uncertainty in data visualizations to screen-reader users. Specifically, we conducted semi-structured interviews, finding that these users prefer to obtain statistical information on uncertainty expressed in plain language, conveyed holistically with avenues to explore the data further in a drilled-down manner. To support screen-reader users in extracting information about uncertainty in online data visualizations, we utilized our findings to enhance VoxLens—an open-source JavaScript plug-in that makes online data visualizations accessible to screen-reader users.