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1125 18th Street, Boulder, CO 80309

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New inferences about users, derived by machine learning algorithms, can create uses for personal data that people might not be able to anticipate or imagine in advance. In this talk I will present early findings from two qualitative studies focusing on the extent to which people are already aware of the inferences systems are making about them, what they believe systems are able to infer, and what they feel systems should be allowed to know about them. I will also describe how people's perceptions of what others allow systems to know about them may be influencing what kind of inferences each individual deems acceptable. Finally, I will argue that there is an inherent social dilemma in systems that rely on machine learning and aggregation of large datasets to derive inferences about people. However, this social dilemma is invisible, which makes negotiating norms for digital privacy much more difficult.

Emilee Rader is an Associate Professor and AT&T Scholar in the Department of Media and Information at Michigan State University. Her research addresses problems that arise at the intersection of people, technology, and information in socio-technical systems. Dr. Rader earned her PhD from the University of Michigan School of Information and spent two years at Northwestern University as a postdoc in the Center for Technology and Social Behavior, where she was a recipient of the highly competitive Computing Innovation post-doctoral fellowship award from the Computing Research Association. She also earned 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 in the early 2000’s 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 human-computer interaction and usable privacy and security venues. 

The Information Science seminar is a weekly talk series and gathering for the Information Science department and its extended community. Any faculty, students, and interested parties regardless of affiliation are welcome. Keep an eye out for future announcements!

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