Fairness Indicators for TensorFlow (TF Dev Summit '20)

Within this TensorFlow Dev Summit demo we will explore two case studies using Fairness Indicators. The first notebook demonstrates an easy way to create and optimize constrained problems using the TFCO library. This method can be useful in improving models when we find that they’re not performing equally well across different slices of our data, which we can identify using Fairness Indicators. Second, we will use Fairness Indivatores with the larger TensorFlow Ecosystem to show how Machine Learning Metadata (MLMD) and the lineage tracking ability can be useful for fairness while working in TensorFlow Extended (TFX). Speakers: Thomas Greenspan - Software Engineer Sean O'Keefe - Software Engineer Jason Mayes - Senior Developer Advocate Resources: Fairness Indicators Lineage Case Study → https://goo.gle/3avg3in Fairness Indicators and TF Constrained Optimization Case Study → https://goo.gle/3bxAgnR TensorFlow Constrained Optimization → https://goo.gle/2WUp5RV FAT* Understanding the Context and Consequences of Pre-trial Detention → https://goo.gle/2ydC0Eh Partnership on AI: Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System → https://goo.gle/2JnnBIf Watch all TensorFlow Dev Summit 2020 sessions → https://goo.gle/TFDS20 Subscribe to the TensorFlow YouTube channel → https://goo.gle/TensorFlow event: TensorFlow Dev Summit 2020; re_ty: Publish; product: TensorFlow - General; fullname: Jason Mayes, Sean O'Keefe;