MISC Talk 9/10, Elissa M. Redmiles, “Learning from the People: From Normative to Descriptive Solutions to Problems in Security, Privacy & Machine Learning”

Title: Learning from the People: From Normative to Descriptive Solutions to Problems in Security, Privacy & Machine Learning

Abstract: 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) selecting security prompts for an online system; (ii) determining which features to include in a classifier for jail sentencing; (iii) defining standards for ethical virtual reality content.

Bio: Elissa Redmiles is an incoming Assistant Professor of Computer Science at Princeton University and a Postdoctoral Researcher at Microsoft Research. Elissa’s research interests are broadly in the areas of security and privacy. She uses computational, economic, and social science methods to understand users’ security and privacy decision-making processes, specifically investigating inequalities that arise in these processes and mitigating those inequalities through the design of systems that facilitate safety equitably across users. Elissa received her Ph.D. in Computer Science from the University of Maryland in 2019. As a graduate student, she was the recipient of the NSF Graduate Research Fellowship, a Facebook Fellowship, and the National Defense Science and Engineering Graduate Fellowship (NDSEG). Her work has appeared in popular press publications such as Scientific American, Business Insider, Newsweek, and CNET and has been recognized with a Distinguished Paper Award at USENIX Security 2018 and the John Karat Usable Privacy and Security Research Award.

Please RSVP by 12PM on 9/7 if you will be there.