Abstract: Although research shows that Internet users care about their privacy, they do not have the ability to read and understand the privacy policies of all the websites that they visit or all the apps that they use. Fixing this gap in online notice and choice is the goal of the Usable Privacy Policy Project, an NSF-funded project to extract salient details from privacy policies and present them to Internet users in ways that are responsive to their needs. I will present my ongoing work as the lead for the project's crowdsourcing and natural language processing efforts. Our results show that crowdworkers can answer questions about privacy policies with high accuracy, and automated methods can be used to identify important details in policy texts, such as statements about data collection and users' privacy options. I will then present some vignettes from my research on online social network privacy and entity linking, along with a long-term goal of 'user-oriented natural language processing' to break down the complex texts that computer users are obligated to read and automatically find the details that affect them the most.

Short Bio: Shomir Wilson is an Assistant Professor of Computer Science in the Department of Electrical Engineering and Computing Systems at the University of Cincinnati. His interests span pure and applied research in natural language processing, privacy, and artificial intelligence. Previously he held postdoctoral and lecturer positions in Carnegie Mellon University's School of Computer Science, and he spent a year as an NSF International Research Fellow at the University of Edinburgh's School of Informatics. He received his Ph.D. in Computer Science from the University of Maryland in 2011.