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Abstract:
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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' adoption decisions. In this talk I showcase 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 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 and suggest user-driven directions for ethically encouraging users to adopt.
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