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What and Who
Title:Learning from the People: From Normative to Descriptive Solutions to Problems in Security, Privacy & Machine Learning
Speaker:Elissa Redmiles
coming from:University of Maryland
Speakers 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 appeared in popular press publications such as Scientific American, Business Insider, Newsweek, and CNET and has been recognized with the John Karat Usable Privacy and Security Student Research Award, a Distinguished Paper Award at USENIX Security 2018, and a University of Maryland Outstanding Graduate Student Award.
Event Type:SWS Colloquium
Visibility:SWS, RG1, MMCI
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Level:AG Audience
Language:English
Date, Time and Location
Date:Tuesday, 13 November 2018
Time:10:30
Duration:60 Minutes
Location:Saarbrücken
Building:E1 5
Room:029
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 and then infer best practice rather than using experts’ normative (prescriptive) determinations to define 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.

Contact
Name(s):Gretchen Gravelle
Phone:0681-93039102
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:YesTo Location:Kaiserslautern
To Building:G26To Room:111
Meeting ID:
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Created:
Gretchen Gravelle/MPI-SWS, 11/02/2018 02:01 PM
Last modified:
Uwe Brahm/MPII/DE, 11/13/2018 07:01 AM
  • Gretchen Gravelle, 11/02/2018 02:08 PM
  • Gretchen Gravelle, 11/02/2018 02:06 PM -- Created document.