Interpretable Machine Learning -- Fairness, Accountability, and Transparency in ML systems

Editor's note: Sayak is a speaker for ODSC West in San Francisco this November! Be sure to check out his talk, "Interpretable Machine Learning -- Fairness, Accountability and Transparency in ML systems," there! The problem is it is much harder to evaluate machine learning systems than to train them. "It requires responsibly requires doing more than just calculating loss metrics. Before putting a model into production, it's critical to audit training data and evaluate predictions for bias."