Google I/O 2019 | Machine Learning Fairness: Lessons Learned

ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and improvements to models, including open source datasets and tools such as TensorFlow Model Analysis. This session will enable developers to proactively think about fairness in product development.


Speakers: Tulsee Doshi and Jacqueline Pan

Session ID: T8ACB1

Previous Algorithms Are Opinions, Not Truth Machines, And Demand The Application Of Ethics
Next Google I/O 2019 | TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow