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

Room 311

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]

Reading 3: Kanthashree Mysore Sathyendra, Shomir Wilson, Florian Schaub, Sebastian Zimmeck, and Norman Sadeh, “Identifying the Provision of Choices in Privacy Policy Text”, Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, Sep 2017 [pdf]

The following websites are also very relevant:
Norberto Andrade
Norberto Andrade

Privacy & Public Policy Manager

Brenda Leong
Brenda Leong

Sr. Counsel &
Dir. of Strategy
Future of Privacy Forum

Norman Sadeh
Norman Sadeh

Carnegie Mellon University