Understanding Bias in Perceiving Dimensionality Reduction Projections

We assume a scenario where a practitioner wants to select DR techniques that produce projections suitable for analyzing their dataset. It is crucial to select projections with high faithfulness for reliable analysis, but practitioners tend to be more biased towards the visual interestingness. We verify the existence of such bias and investigate its underlying causes, providing a grounded basis for mitigating the biases. 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.