Understanding Bias in Perceiving Dimensionality Reduction Projections
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912505436045312 |
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| author | Doh, Seoyoung Jeon, Hyeon Shin, Sungbok Quadri, Ghulam Jilani Kim, Nam Wook Seo, Jinwook |
| author_facet | Doh, Seoyoung Jeon, Hyeon Shin, Sungbok Quadri, Ghulam Jilani Kim, Nam Wook Seo, Jinwook |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Bias in Perceiving Dimensionality Reduction Projections Doh, Seoyoung Jeon, Hyeon Shin, Sungbok Quadri, Ghulam Jilani Kim, Nam Wook Seo, Jinwook Human-Computer Interaction Machine Learning 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. |
| title | Understanding Bias in Perceiving Dimensionality Reduction Projections |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2507.20805 |