[null,null,["最后更新时间 (UTC):2024-11-14。"],[[["Fairness in machine learning aims to address potential unequal outcomes for users based on sensitive attributes like race, gender, or income due to algorithmic decisions."],["Machine learning systems can inherit human biases, impacting outcomes for certain groups, and require strategies for identification, measurement, and mitigation."],["Google has worked on improving fairness in products like Google Search and Google Photos by utilizing the Monk Skin Tone Scale to better represent skin tone diversity."],["Developers can learn about fairness and bias mitigation techniques in detail through resources like the Fairness module of Google's Machine Learning Crash Course and interactive AI Explorables from People + AI Research (PAIR)."]]],[]]