Rayid Ghani

Rayid Ghani (born 1977) is a Distinguished Career Professor in the Machine Learning Department (in the School of Computer Science) and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. Previously, he was the director of the Center for Data Science and Public Policy, research associate professor in the department of computer science, and a senior fellow at the Harris School of Public Policy at the University of Chicago. He was also the co-founder of Edgeflip, an analytics startup that grew out of the Obama 2012 Campaign, focused on social media products for non-profits, advocacy groups, and charities. In September 2019, it was announced that he will be leaving the University of Chicago and joining Carnegie Mellon University's School of Computer Science and Heinz College of Information Systems and Public Policy|. Prior to that, Rayid was the Chief Scientist of the Obama 2012 Election Campaign and focused on using data science, machine learning, and technology to improve fundraising, volunteer mobilization, voter registration, persuasion, and turnout. Quotes I realized two things It’s doable at the massive scale of the campaign, and that means it’s doable in the context of other problems.” One thing I think we should realize is, we'll be calling the AI today what they used to call AI 10 years ago. AI 10 years ago was a bunch of rules. You would write thousands of rules and that was AI. And that's embedded in pretty much every system we consume today. Every service we're getting-- simple things like health insurance. A typical high-rate insurance company has about half a million rules inside the system to process your claim. And so AI has always been in there; it's just as soon as it becomes deployed, it's not called AI because it's now real, it's not magic. So I think the way to think about it isn't whether it's going to help or not, or does it do something well or not, but does it do it better than what we're doing today? And I think that gets to deepest point of values. If we take a problem and we solve it and now it's 90% correct, well what does that mean? Do we only care about overall correctness, or do we care about how does it have disparate impact on different types of people? If it's 10% wrong, is it 10% wrong on everybody External links Category: living people Category:Computer scientists from the United States Category:Academics from the United States Category:Carnegie Mellon University alumni Category:Carnegie Mellon University faculty Category:University of Chicago faculty Category:1977 births Category:Immigrants to the United States Category:People from Pakistan