Abstract:
“Notice and choice,” the dominant model for governing digital data collection and use, assumes that if proper transparency is provided people will only use platforms that have data practices they agree with. But, in reality, widespread data collection and use of machine learning enables inferences to be generated that are hard for people to anticipate. This talk describes my recent work focused on people’s beliefs and expectations about what data are collected about them and how those data are used, and makes an argument about how specific limits on data collection and inferences could help people make privacy choices that are more aligned with their preferences.
Bio:
Dr. Emilee Rader is an Associate Professor in the Department of Media and Information at Michigan State University. She studies how people reason and make choices about data collection and inferences enabled by digital technologies, to better understand why people struggle to manage their privacy, and to discover new ways to help people gain more appropriate control over information about them. Dr. Rader earned her PhD from the University of Michigan School of Information and spent two years at Northwestern University in the Department of Communication Studies, where she was a recipient of the highly competitive Computing Innovation post-doctoral fellowship award from the Computing Research Association. She also has a professional Master’s degree from the Human Computer Interaction Institute at Carnegie Mellon University, and worked with an interdisciplinary team of researchers at Motorola Labs designing and evaluating applications for mobile technologies. Her work has been funded by several grants from the National Science Foundation, and she primarily publishes in usable privacy and security and human-computer interaction venues.