Big data and artificial intelligence are growing more pervasive and are creating new, complex links between individuals and the many groups to which they might belong, including groups no one might have thought of as a “group” before. How should we think about questions of group privacy, discrimination, and group identity in this new world? Does it matter whether algorithms used in health care focus on identified groups that have been designated as protected classes, rather than more precisely (or amorphously) defined groups that may or may not align with some protected class boundaries? What does it mean to protect the privacy of an individual when big data and algorithms can predict many things from membership in groups (and group membership from many individual things)? How should providers, bioethicists, data scientists, and legal scholars take account of these changing issues? The panel will discuss these questions and more at the intersection of groups, genetics, and AI.