Color, Gender, and Bias: Examining the Role of Stereotyped Colors in Visualization-Driven Pay Decisions
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arXiv
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| Format: | Preprint |
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2025
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| author | Cabric, Florent Bjarnadottir, Margret Vilborg Isenberg, Petra |
| author_facet | Cabric, Florent Bjarnadottir, Margret Vilborg Isenberg, Petra |
| contents | We investigate the impact of stereotyped gender-color associations in a visualization-driven decision-making task. In the context of gender data visualization, the well-known "pink for girls and blue for boys" color assignment is associated with stereotypes that could bias readers and decision-makers. Understanding the effects of using stereotyped colors in visualizations for decision-making can help designers better choose colors in stereotype-prone contexts. We therefore explore the potential impact of stereotyped colors on compensation decision-making through two crowdsourced experiments. In these experiments, we evaluate how the association of color with gender (stereotyped vs non-stereotyped) affects the user's allocation decisions in the context of salary adjustments. Our results indicate that explicit expression of the color-gender associations, in the form of a legend on the data visualization, leads to in-group favoritism. However, in the absence of a legend, this in-group favoritism disappears, and a small effect of non-stereotyped colors is observed. A free copy of this paper with all supplemental materials is available at https://osf.io/d4q3v/?view_only=22b636d6f7bb4a7991d9576933b3aaad |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Color, Gender, and Bias: Examining the Role of Stereotyped Colors in Visualization-Driven Pay Decisions Cabric, Florent Bjarnadottir, Margret Vilborg Isenberg, Petra Human-Computer Interaction We investigate the impact of stereotyped gender-color associations in a visualization-driven decision-making task. In the context of gender data visualization, the well-known "pink for girls and blue for boys" color assignment is associated with stereotypes that could bias readers and decision-makers. Understanding the effects of using stereotyped colors in visualizations for decision-making can help designers better choose colors in stereotype-prone contexts. We therefore explore the potential impact of stereotyped colors on compensation decision-making through two crowdsourced experiments. In these experiments, we evaluate how the association of color with gender (stereotyped vs non-stereotyped) affects the user's allocation decisions in the context of salary adjustments. Our results indicate that explicit expression of the color-gender associations, in the form of a legend on the data visualization, leads to in-group favoritism. However, in the absence of a legend, this in-group favoritism disappears, and a small effect of non-stereotyped colors is observed. A free copy of this paper with all supplemental materials is available at https://osf.io/d4q3v/?view_only=22b636d6f7bb4a7991d9576933b3aaad |
| title | Color, Gender, and Bias: Examining the Role of Stereotyped Colors in Visualization-Driven Pay Decisions |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.24999 |