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, Rayid saw that while automated predictions can be helpful, they don't always paint a full picture. The key is the context and details required when making tough decisions involving humans and their lives. In our conversation, we delve into the world of explainability methods, including the necessary human involvement, machine feedback loop and how he is working to effectively solve problems in the world today.
