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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.

Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.

Jul 18, 2019

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