A data scientist is finalizing a presentation for a government regulatory body and internal executive stakeholders. The presentation's central element is a heat map visualizing model performance degradation across demographic segments. The current visualization uses a traditional red-to-green diverging color scale to represent poor to strong performance, respectively. An accessibility audit flagged this choice as non-compliant for users with deuteranopia. To ensure the chart is fully accessible and clearly communicates insights to all viewers, which action is the most appropriate for the data scientist to take?
Supplement the heat map with a detailed data table in an appendix and add an accessibility note directing users to this table for the raw values.
Adjust the saturation and brightness of the existing red and green colors until the contrast ratio between them meets the WCAG 2.1 AA requirement of 3:1 for graphical objects.
Replace the red-green scale with a perceptually uniform, colorblind-safe diverging palette, such as blue-to-orange, and verify that adjacent colors meet a 3:1 contrast ratio.
Convert the visualization to use a single-hue sequential color palette, such as 'viridis', varying lightness from light to dark to represent the performance metric.
The correct answer is to replace the red-to-green scale with a colorblind-safe diverging palette and ensure it meets contrast standards. The use of red and green together is the primary issue, as it is indistinguishable for users with deuteranopia and protanopia, the most common forms of color vision deficiency. The best practice is to select a diverging palette specifically designed for accessibility, such as one that uses blue and orange hues. Ensuring a 3:1 contrast ratio between adjacent colors addresses WCAG 2.1 AA guidelines for graphical objects.
Simply adjusting the contrast of the existing red and green colors is insufficient because it does not solve the underlying problem of hue discrimination for colorblind users. Converting to a sequential palette is inappropriate because the data is diverging (showing deviation in two directions from a neutral midpoint), and a sequential scale would misrepresent the nature of the data. Providing the data in a separate table is a useful supplement but does not fix the accessibility of the primary visualization itself, which should be the main goal.
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