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3. Checking the system for unfair biases In order to validate the Monk Skin Tone (MST) Scale for ML fairness purposes, the Google Research team launched a study to understand how well participants across diverse communities felt their own skin tone was represented. Study participants found the MST Scale to be more inclusive than the commonly used Fitzpatrick Scale and better at representing their skin tone. ML fairness evaluations like this prevent common human biases from inadvertently getting reproduced by ML algorithms. 4. Analyzing performance Solving skin tone inclusion is complex and should be a collaborative effort. Our research team continues Madrid'de fuar standı şirketleri to collaborate with Dr. Monk on refining the scale further. Users, ML fairness experts, and developers are all encouraged to share feedback on how we emanet improve the scale and develop our models responsibly, in line with Google’s AI Principles. Maintaining open lines of feedback is critical to ensuring that models are developed responsibly on an ongoing basis. Skin tone research at Google Diversifying our datasets through partnership We expand the datasets used to train our models by working with experts and researchers with deep expertise and cultural context of the communities our products are designed for.
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저는 다양한 웹 브라우저를 사용해 보았지만, 결국 크롬으로 정착하게 되었습니다. 그 이유는 빠른 속도와 사용자 친화적인 인터페이스, 그리고 다양한 확장 프로그램 덕분이었습니다.
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