Explained: Google’s New Skin Tone Scale For Refined Search Results And More

Among the multiple artificial intelligence (AI) and machine learning (ML) initiatives announced by Google last week at its annual I/O developer conference were a number of projects to train the internet company’s AI platforms.One such development was the introduction of a 10-shade monk Skin Tone (MST) scale – something that could have broader sociological significance. as technology platforms demonstrate deeper intersections with society at large. In Google’s words, the MST scale will “support inclusive products and research across the industry.”

What is the Monk Skin Tone Scale?

Developed in partnership with Dr. Ellis Monk, an associate professor of sociology at Harvard University, the Monk Skin Tone Scale (MST) is a tool that will primarily be incorporated by Google into computer vision, which is a type of AI that allows computers to see and understand images. It has been found that computer vision systems often do not work as effectively for people with darker skin tones than for those with lighter skin tones. By using the MST scale, Google and the tech industry aim to create more representative datasets so that these AI models can be trained to identify a wider range of skin tones in images.

How will this work?

According to Google, the scale will “make it easier for people from all walks of life to find more relevant and useful search results.” For example, users who search for makeup or beauty tutorials in Google Images will see an option to further narrow search results by skin tone. In the future, Google will use the MST scale to better detect and categorize images to give a greater range of results.

The tech giant plans to further expand the use of this schema – the database structure created based on different skin tones – so that creators and online businesses can tag their content or products by based on other attributes, such as hair color and texture. Google has openly released the scale so anyone can use it for product research and development.

Why the MST scale?

According to Dr. Courtney Heldreth, social psychologist and user experience (UX) researcher at Google’s Responsible AI Human-Centered Technology UX department, “Persistent inequalities exist globally due to prejudice or discrimination against people with darker skin tones, also known as colorism”. And AI not seeing skin tone accurately, which could lead to existing unevenness, is a type of colorism.

To fill this gap, a Google research team including Heldreth and Xango Eyeé, a product manager working on responsible AI, focused on bringing more equity to AI development. Last year, the team partnered with Monk, whose research has focused on how factors such as skin tone, race and ethnicity affect inequality.

The foundations of the MST scale were laid on the existing Fitzpatrick scale, developed by American dermatologist Thomas B Fitzpatrick in 1975, which classified human skin type into seven broad colors. The Google team and Monk arrived at a scale consisting of 10 shades – a range considered not too limiting but not too complex – and surveyed thousands of adults in the United States who felt more represented with the new methodology.

Are there similar developments elsewhere?

Several major companies have stepped up their efforts for color-based inclusion in light of the 2020 Black Lives Matter protests, which occurred in the wake of police brutality and the killing of George Floyd, an African American. 46 years old. In 2020, Band-Aid, owned by Johnson & Johnson, launched a new line of adhesive bandages in varying shades of black and brown to make its products more inclusive for people of color. In India, after backlash and strong public comment on discrimination against people with darker skin tones, the Advertising Standards Council of India (ASCI) in 2014 issued guidelines for advertising lightening and fair trade products for the skin. Most recently, in 2020, Hindustan Unilever-owned cosmetics brand Fair & Lovely was rebranded as Glow & Lovely, after criticism for promoting colorism in its advertisements and marketing campaigns.

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