This panel will explore the data science behind ML and AI. Along with privacy experts discussing ethical considerations, there is a need for general understanding of the data science that potentially makes ML/AI qualitatively different from prior technology re: privacy concerns. This “tech primer” will consider these terms, along with discussing concepts such as neural networks, supervised/unsupervised learning, derived data, and others; and related P+S issues.
Brenda Leong, Senior Counsel and Director of Strategy, Future of Privacy Forum
Norman Sadeh, Professor, Carnegie Mellon University
Norberto Andrade, Privacy and Public Policy, Facebook
Sarah Holland, Public Policy Manager, Google
Reading 1: B. Liu, M.S. Andersen, F. Schaub, H. Almuhimedi, S. Zhang, N. Sadeh, A. Acquisti, and Y. Agarwal, “Follow My Recommendations: A Personalized Assistant for Mobile App Permissions”, Symposium on Usable Privacy and Security (SOUPS ’16), Jun 2016 Denver, CO [pdf]
Reading 2: S. Zimmeck, Z. Wang, L. Zou, R. Iyengar, B. Liu, F. Schaub, S. Wilson, N. Sadeh, S.M. Bellovin, J.R. Reidenberg, “Automated Analysis of Privacy Requirements for Mobile Apps”, NDSS’17: Network and Distributed System Security Symposium, Feb 2017 [pdf]