Elissa Redmiles
March 8, 2023 at 11:00 AM on Zoom / Soda Hall
Learning from the People: Responsibly Encouraging Adoption of Privacy-Sensitive Contact Tracing Apps
Abstract: At the beginning of the pandemic contact tracing apps proliferated as a potential solution to scaling infection tracking and response. While significant focus was put on developing privacy protocols for these apps, relatively less attention was given to understanding why, and why not, users might adopt these privacy-sensitive technologies. Yet, for these technological solutions to benefit public health, users must be willing to adopt these apps. In this talk I showcase the value of taking a descriptive ethics approach to setting best practices in ethically encouraging adoption of privacy-sensitive social-good technologies. 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 user's decision inputs 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, I show how high the bar is for achieving adoption. I conclude by discussing a large-scale field study in which we put our survey and experimental results into practice to help the state of Louisiana advertise their COVID app through a series of randomized controlled Google Ads experiments. These experiments identify a differential effect from including information about privacy & data collection practices when a privacy-sensitive technology is described as benefiting individual vs. collective good.
Bio: Dr. Elissa M. Redmiles is a faculty member and research group leader at the Max Planck Institute for Software Systems and a Visiting Scholar at the Berkman Klein Center for Internet & Society at Harvard University. She uses computational, economic, and social science methods to understand users’ security, privacy, and online safety-related decision-making processes and remedy inequities in those processes. Her work has received several paper awards and recognitions at USENIX Security, ACM CCS, ACM CHI, ACM EAAMO, and ACM CSCW and has been featured in popular press publications such as the New York Times, Wall Street Journal, Scientific American, Rolling Stone, Wired, Business Insider, and CNET.