oct
16
2018

CITP Luncheon Speaker Series:
Elissa M. Redmiles – Learning from the People:
From Normative to Descriptive Solutions to Problems in Security, Privacy & Machine Learning

CITP Luncheon Series

Date: Tuesday, October 16, 2018
Time: 12:30 p.m.
Location: 306 Sherrerd Hall

No RSVP required for current Princeton faculty, staff, and students. Open to members of the public by invitation only. Please contact Jean Butcher at if you are interested in attending a particular lunch.

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 a Ph.D. candidate in computer science at the University of Maryland and has been a visiting researcher with the Max Planck Institute for Software Systems and the University of Zurich. 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 is the recipient of a NSF Graduate Research Fellowship, a National Science Defense and Engineering Graduate Fellowship, and a Facebook Fellowship. Her work has been recognized with the John Karat Usable Privacy and Security Student Research Award, a Distinguished Paper Award at USENIX Security 2018, a Best Presentation Honorable Mention at EC2018, Most Engaging Talk at USENIX Security HotSec 2017, and a University of Maryland Outstanding Graduate Student Award. Additionally, Elissa’s work has been featured in popular press publications such as Scientific American, Business Insider, Newsweek, and CNET.

To request accommodations for a disability, please contact Jean Butcher, , 609-258-9658 at least one week prior to the event.