How can you design data visualizations for users with color blindness in ML models?

1 Use color palettes that work for all

One of the simplest ways to improve your data visualizations for color blind users is to choose colors that are easy to differentiate for most types of color blindness. For example, avoid using red and green together, as they are often confused by people with protanopia or deuteranopia. Instead, use colors that contrast well, such as blue and orange, or purple and yellow. You can also use tools like ColorBrewer or Color Oracle to test and select color palettes that are suitable for different vision impairments.

3 Use labels, legends, and annotations

A third way to improve your data visualizations for color blind users is to use labels, legends, and annotations to provide more information and context to your charts and graphs. Labels are text that identify the axes, titles, or data points in your visualization. Legends are boxes that explain the meaning of the colors, patterns, textures, or symbols used in your visualization. Annotations are notes that highlight or explain specific features or trends in your visualization. By using labels, legends, and annotations, you can make your data visualizations more clear and comprehensible for color blind users.

4 Avoid using color alone to convey meaning

A fourth way to improve your data visualizations for color blind users is to avoid using color alone to convey meaning or importance in your charts and graphs. Color can be a useful way to emphasize or differentiate data, but it should not be the only way. If you rely solely on color to convey meaning, you risk losing or confusing your color blind users. Instead, use other visual elements, such as size, shape, position, or text, to reinforce or supplement the message that color conveys. For example, you can use larger or smaller circles to show the magnitude of data points in a scatter plot, or use arrows or labels to show the direction or significance of data trends in a line chart.

5 Test your data visualizations with color blind users

A fifth way to improve your data visualizations for color blind users is to test your charts and graphs with color blind users or simulate their vision with tools or apps. Testing your data visualizations with color blind users can help you get feedback and suggestions on how to make your charts and graphs more accessible and effective. Simulating their vision with tools or apps can help you check and improve the color contrast, clarity, and readability of your data visualizations. Some examples of tools or apps that can help you test or simulate color blindness are Color Blindness Simulator, Color Blind Check, or Sim Daltonism.

6 Follow data visualization best practices

A sixth way to improve your data visualizations for color blind users is to follow data visualization best practices that apply to all users, regardless of their vision. Data visualization best practices are guidelines that help you create charts and graphs that are accurate, informative, and engaging. Some examples of data visualization best practices are choosing the right type of chart for your data, using clear and consistent labels and legends, avoiding clutter and distortion, and telling a story with your data. By following data visualization best practices, you can ensure that your data visualizations are not only accessible for color blind users, but also effective for all users.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?