Speaker: Carlos Scheidegger, Department of Computer Science, University of Arizona
Presentation Title: Data Science, Humanely
Abstract: It is undeniable that machine learning has fundamentally changed what computers can do, especially as access to data sources and processing power continues to become easier. At the same time, the ability for us humans to actually make sense of these techniques has not progressed at nearly the same pace. In this talk, I will present two projects on the intersection of machine learning and society. First, I will present some work in how to model, detect, and correct disparate impact in machine-learning processes. Second, I will describe one serious way in which predictive policing—the idea that police work can be allocated automatically and predictively through machine learning—can go wrong.
Bio: Since 2014, Carlos Scheidegger is an assistant professor in the Department of Computer Science at the University of Arizona. He holds a PhD in Computing from the University of Utah, where he worked on software infrastructure for scientific collaboration. His current research interests are in large-scale data analysis, information visualization and, more broadly, what happens "when people meet data", for both good and bad. His research has been supported by both industry and the government through awards from the NSF and AT&T Labs, and his honors include multiple best paper awards including at the IEEE Visualization conference, and an IBM student fellowship.