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Teaching algorithms about skin tones

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When Ellis Monk’s wife became pregnant in 2019, the couple became curious about what skin tone their child might have. The subject was of more than passing interest to the sociology professor, some of whose work involves the role lighter and darker skin tones play in society. Monk’s wife noted that a comprehensive scale would be useful and urged him to develop one.

So he did, and last month Google adopted Monk’s namesake 10-shade scale as a standard in its digital products to make them more inclusive and diverse and to promote wider awareness of the problems and unintended bias associated with technologies that fail to recognize a wider range of skin tones.

Monk’s scale is already making an impact. It has been incorporated in Google’s online image searches and photo filters. The innovation will be particularly important for training artificial intelligence and machine learning applications, such as facial recognition and self-driving vehicle systems, which often have not performed as well with people with darker skin tones. And it could help reduce or eliminate some unintended algorithm bias in search engines and other products.

“She encouraged me to keep it going,” Monk said, crediting his wife. “Eventually, I delved in and put together the Monk Skin Tone Scale, taking the baton [and] building on what she had started.”

Monk’s scale is a rethinking of the Fitzpatrick Scale, which has been considered the online standard. Created by dermatologist Thomas B. Fitzpatrick at Harvard Medical School in 1975, the Fitzpatrick Scale was developed to classify six tones based on skin pigmentation and response to sun exposure. It was not created with diverse populations in mind but as a gauge of tolerance for ultraviolet light, Monk explained.

Source link The Harvard

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