Responsibly Encouraging Adoption of Contact Tracing Apps
Join us online for the next lecture in our series on Digital Humanism—this time with Elissa M. Redmiles from Microsoft Research.
This is an online-only event.
See description for details.
Learning from the People: Responsibly Encouraging Adoption of Contact Tracing Apps
Elissa M. Redmiles (Microsoft Research)
moderated by James Larus (EPFL, Switzerland)
Tuesday, October 20, 2020 at 5:00 P.M. (17:00) CEST
To participate, go to the following link, Password: 0dzqxqiy.
About the Series
Digital humanism deals with the complex relationship between man and machine. It acknowledges the potential of Informatics and IT. At the same time, it points to related apparent threats such as privacy violations, ethical concerns with AI, automation, and loss of jobs, and the ongoing monopolization on the Web. The Corona crisis has shown these two faces of the accelerated digitalization—we are in a crucial moment in time.
For this reason, we started a new initiative—DIGHUM lectures—with regular online events to discuss the different aspects of Digital Humanism. We will have a speaker on a specific topic (30 minutes) followed by a discussion of 30 minutes every second Tuesday of each month at 5:00 PM CEST. This crisis does seriously affect our mobility, but it also offers the possibility to participate in events from all over the world—let’s take this chance to meet virtually.
Abstract: Learning from the People: Responsibly Encouraging Adoption of Contact Tracing Apps
A growing number of contact tracing apps are being developed to complement manual contact tracing. Yet, for these technological solutions to benefit public health, users must be willing to adopt these apps. While privacy was the main consideration of experts at the start of contact tracing app development, privacy is only one of many factors in users’ decision to adopt these apps. In this talk, Elissa M. Redmiles showcases the value of taking a descriptive ethics approach to setting best practices in this new domain. Descriptive ethics, introduced by the field of moral philosophy, determines best practices by learning directly from the user—observing people’s preferences and inferring best practice from that behavior—instead of exclusively relying on experts’ normative decisions.
This talk presents an empirically-validated framework of the inputs that factor into a user’s decision to adopt COVID19 contact tracing apps, including app accuracy, privacy, benefits, and mobile costs. Using predictive models of users’ likelihood to install COVID apps based on quantifications of these factors, Elissa M. Redmiles shows how high the bar is for these apps to achieve adoption and suggest user-driven directions for ethically encouraging adoption.