Today we’re joined by Rayid Ghani, Director of the Center for Data Science and Public Policy at the University of Chicago. Rayid’s goal is to combine his skills in machine learning and data with his desire to improve public policy and the social sector. Drawing on his range of experience from the corporate world to Chief Scientist for the 2012 Obama Campaign, we delve into the world of automated predictions and explainability methods. Here we discuss: How automated predictions can be helpful, but they don’t always paint a full picture  When dealing with public policy and the social sector, the key to an effective explainability method is the correct context Machine feedback loops that help humans override the wrong predictions and reinforce the right ones Supporting proactive intervention through complex explanability tools