Understanding Bias in Perceiving Dimensionality Reduction Projections
This research addresses a bias in visual analytics that can lead to unreliable interpretations in data analysis, though it is incremental in explaining and mitigating an existing issue.
The study tackled the problem of practitioners favoring aesthetically pleasing dimensionality reduction projections over structurally faithful ones, a bias termed visual interestingness, and found that this bias intensifies with color-encoded labels and shorter exposure times.
Selecting the dimensionality reduction technique that faithfully represents the structure is essential for reliable visual communication and analytics. In reality, however, practitioners favor projections for other attractions, such as aesthetics and visual saliency, over the projection's structural faithfulness, a bias we define as visual interestingness. In this research, we conduct a user study that (1) verifies the existence of such bias and (2) explains why the bias exists. Our study suggests that visual interestingness biases practitioners' preferences when selecting projections for analysis, and this bias intensifies with color-encoded labels and shorter exposure time. Based on our findings, we discuss strategies to mitigate bias in perceiving and interpreting DR projections.