  Researchers find weaknesses in current strategies for protecting privacy in
large datasets. Many anonymized datasets are reidentifiable, and norms for
offering data subjects notice and consent over emphasize individual
responsibility. Based on fieldwork with data managers in the City of Seattle, I
identify ways that these conventional approaches break down in practice.
Drawing on work from theorists in sociocultural anthropology, I propose that a
Human Centered Data Science move beyond concepts like dataset identifiability
and sensitivity toward a broader ontology of who is implicated by a dataset,
and new ways of anticipating how data can be combined and used.
