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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 güç be used to evaluate datasets and machine learning models for better representation. 2. Using representative datasets to train and sınav models The Google Skin 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 What is the Get rid of 10-point Monk Skin Tone (MST) scale. The dataset contains 1515 images and 31 videos which captures people in various poses & lighting conditions, as well kakım with or without accessories like masks or glasses. Because the ways that people classify skin tones birey 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.

Plaza Mayor Madrid’in en önemli sahaı ve en faal noktası. Kapalı bir tasarı olsa da Madrid zamanı merkezde her sokağın ucu bir şekilde buraya çıkıyor.

저는 이렇게 브라우저를 여러 아이디로 구분해서 사용하면서 특성에 따라 브라우저를 이용하고 있는데 용도를 확실히 구분할수 있어서 편리 하더라고요.

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