  A subset of machine learning research intersects with societal issues,
including fairness, accountability and transparency, as well as the use of
machine learning for social good. In this work, we analyze the scholars
contributing to this research at the intersection of machine learning and
society through the lens of the sociology of science. By analyzing the
authorship of all machine learning papers posted to arXiv, we show that
compared to researchers from overrepresented backgrounds (defined by gender and
race/ethnicity), researchers from underrepresented backgrounds are more likely
to conduct research at this intersection than other kinds of machine learning
research. This state of affairs leads to contention between two perspectives on
insiders and outsiders in the scientific enterprise: outsiders being those
outside the group being studied, and outsiders being those who have not
participated as researchers in an area historically. This contention manifests
as an epistemic question on the validity of knowledge derived from lived
experience in machine learning research, and predicts boundary work that we see
in a real-world example.
