VIRTUAL SEMINAR - ZOOM MEETING LINK
Link: https://umich.zoom.us/j/99929959678
Passcode: csaaw

Abstract
Personal data are not discrete in socially-networked digital environments. A user who consents to allow access to their profile can expose the personal data of their network connections to non-consented access. Therefore, the traditional consent model (informed and individual) is not appropriate in social networks where informed consent may not be possible for all users affected by data processing and where information is distributed across users. This talk will outline the adequacy of consent for data transactions. Informed by the shortcomings of individual consent, we introduce both a platform-specific model of ``distributed consent'' and a cross-platform model of a ``consent passport.'' In both models, individuals and groups can coordinate by giving consent conditional on that of their network connections. We simulate the impact of these distributed consent models on the observability of social networks and find that low adoption would allow macroscopic subsets of networks to preserve their connectivity and privacy.

Speaker bio
Juniper Lovato (pronouns: she/her) is an educator and researcher in the field of complex systems and data science. Her current research focuses on Data Ethics, Group Privacy, Open Source, Complex Systems, and Data Science. She is the Director of partnerships and external programs at the Vermont Complex Systems Center at UVM where she organizes Complex Systems programs and thinks about data ethics. She is also a Ph.D. student in Complex Systems & Data Science, at the University of Vermont. Previously, she was the Director of Education for the Santa Fe Institute in Santa Fe, New Mexico where she was born and raised (on a rural family compound (in the Village of Tesuque) populated by hippies and Spanish/Swedish folk musicians, yup they had a dance pavilion). In her free time, she makes/supports makerspaces and open source/open science programs.