Venue: online

Time: 17:30 – 18:30. The lecture will be approximately 45 minutes, after which there will be time for questions.

Registration: Registration is open.

This talk is part of the Secure Systems Demo Day 2020 program.

Description: A variety of experts — computer scientists, policy makers, judges — constantly make decisions about best practices for computational systems. They decide which features are fair to use in a machine learning classifier predicting whether someone will commit a crime, and which security behaviors to recommend and require from end-users. Yet, the best decision is not always clear. Studies have shown that experts often disagree with each other, and, perhaps more importantly, with the people for whom they are making these decisions: the users.

This raises a question: Is it possible to learn best-practices directly from the users? The field of moral philosophy suggests yes, through the process of descriptive decision-making, in which we observe people’s preferences from which to infer best practice rather than using experts’ normative (prescriptive) determinations of best practice. In this talk, I will explore the benefits and challenges of applying such a descriptive approach to making computationally-relevant decisions regarding: (i) optimizing security prompts for an online system; (ii) determining which features are fair to include in a classifier and which decision makers should evaluate fairness; (iii) defining standards for ethical virtual reality content.

About the speaker: Elissa M. Redmiles is a Faculty Member and Research Group Leader of the Digital Harm group at the Max Planck Institute for Software Systems. She additionally serves as a consultant and researcher at multiple institutions, including Microsoft Research and Facebook. Dr. Redmiles uses computational, economic, and social science methods to understand users’ security, privacy, and online safety-related decision-making processes. Much of her work focuses specifically on investigating inequalities that arise in these decision-making processes and mitigating those inequalities through the design of systems that facilitate safety equitably across users. Dr. Redmiles’ work has been featured in popular press publications such as Scientific American, Wired, Business Insider, Newsweek, Schneier on Security, and CNET and has been recognized with multiple Distinguished Paper Awards at USENIX Security as well as the John Karat Usable Privacy and Security Research Award. Dr. Redmiles received her B.S. (Cum Laude), M.S., and Ph.D. in Computer Science from the University of Maryland. As a graduate student, she was supported by a NSF Graduate Research Fellowship, a National Defense Science and Engineering Graduate Fellowship, and a Facebook Fellowship.