Improving skin tone representation across Google

Using the Monk Skin Tone Scale to improve Google products

Updating our approach to skin tone can help us better understand representation in imagery, as well as evaluate whether a product or feature works well across a range of skin tones. This is especially important for computer vision, a type of AI that allows computers to see and understand images. When not built and tested intentionally to include a broad range of skin-tones, computer vision systems have been found to not perform as well for people with darker skin.

The MST Scale will help us and the tech industry at large build more representative datasets so we can train and evaluate AI models for fairness, resulting in features and products that work better for everyone — of all skin tones. For example, we use the scale to evaluate and improve the models that detect faces in images.

Here are other ways you’ll see this show up in Google products.

Improving skin tone representation in Search

Every day, millions of people search the web expecting to find images that reflect their specific needs. That’s why we’re also introducing new features using the MST Scale to make it easier for people of all backgrounds to find more relevant and helpful results.

For example, now when you search for makeup related queries in Google Images, you’ll see an option to further refine your results by skin tone. So if you’re looking for “everyday eyeshadow” or “bridal makeup looks” you’ll more easily find results that work better for your needs.

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