  With the rise of AI and data mining techniques, group profiling and
group-level analysis have been increasingly used in many domains including
policy making and direct marketing. In some cases, the statistics extracted
from data may provide insights to a group's shared characteristics; in others,
the group-level analysis can lead to problems including stereotyping and
systematic oppression. How can analytic tools facilitate a more conscientious
process in group analysis? In this work, we identify a set of accountable group
analytics design guidelines to explicate the needs for group differentiation
and preventing overgeneralization of a group. Following the design guidelines,
we develop TribalGram, a visual analytic suite that leverages interpretable
machine learning algorithms and visualization to offer inference assessment,
model explanation, data corroboration, and sense-making. Through the interviews
with domain experts, we showcase how our design and tools can bring a richer
understanding of "groups" mined from the data.
