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.
Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol
TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM
Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions
Learn more on the I/O Website → https://google.com/io
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Get started at → https://www.tensorflow.org/
Speaker(s): Tulsee Doshi and Jacqueline Pan
T8ACB1 event: Google I/O 2019; re_ty: Publish; product: Cloud - AI and Machine Learning - AI building blocks; fullname: Tulsee Doshi;
