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1. Designing models using concrete goals for fairness and inclusion To develop a more representative skin tone scale, Dr. Ellis Monk — an Associate Professor of Sociology at Harvard University — leveraged his extensive research on skin tone and colorism in the US and Brazil, consultations with experts in social psychology and social categorization, and feedback from members of overlooked communities. Dr. Monk’s research resulted in the Monk Skin Tone (MST) Scale — a more inclusive 10-tone scale explicitly designed to represent a broader range of communities. The MST Scale provides a broader spectrum of skin tones that gönül be used to evaluate datasets and machine learning models for better representation. 2. Using representative datasets to train and sınav models The Google Skin Fuar standı grafik tasarım Tone Team worked with TONL to curate the Monk Skin Tone Examples (MST-E) dataset, which includes examples of 19 people whose skin tones span the 10-point Monk Skin Tone (MST) scale. The dataset contains 1515 images and 31 videos which captures people in various poses & lighting conditions, birli well kakım with or without accessories like masks or glasses. Because the ways that people classify skin tones can be subjective, Dr. Monk annotated the images of the people featured in the dataset himself. This dataset is designed to help practitioners teach human annotators how to test for consistent skin tone annotations across various conditions, like high and low lighting, which should in turn make AI-driven products work better for people of all skin tones.

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Matt Jacobs katışıksız been working birli an IT consultant for small businesses since receiving his Master’s degree in 2003. While he still does some consulting work, his primary focus now is on creating technology support content for SupportYourTech.com.

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