With the rapid growth of the use of artificial intelligence in online shopping, we can’t help but still notice the challenge when it comes to skintone matching accuracy for beauty products. However, things may be taking a more positive turn with Google’s latest research.
Google recently revealed that they are conducting a skintone research with Harvard University associate professor Ellis Monk to make products more inclusive and diverse. The research aims to further enhance Monk’s Skin Tone Scale (MST) which uses use artificial intelligence to create an updated skintone metric that combats light skintone bias.
Monk Skin Tone Scale
The MST is a 10-point skintone scale that aims to be more representative of those with deeper skintones. It provides a huge contrast from the more popular Fitzpatrick scale which was developed in the ‘70s and was initially used to simply demonstrate how fair skin would look with a tan.
This scale is not only good for possibly expanding AI-powered skintone matching in beauty shopping but also aims to change the game when it comes to skin type assessment in dermatology, as well as improved app development for other skin-related concerns.
Google explains that the scale is still in further development. But it’s a work in progress that we’ll gladly stay tuned to.
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