  Despite interest in communicating ethical problems and social contexts within
the undergraduate curriculum to advance Public Interest Technology (PIT) goals,
interventions at the graduate level remain largely unexplored. This may be due
to the conflicting ways through which distinct Artificial Intelligence (AI)
research tracks conceive of their interface with social contexts. In this paper
we track the historical emergence of sociotechnical inquiry in three distinct
subfields of AI research: AI Safety, Fair Machine Learning (Fair ML) and
Human-in-the-Loop (HIL) Autonomy. We show that for each subfield, perceptions
of PIT stem from the particular dangers faced by past integration of technical
systems within a normative social order. We further interrogate how these
histories dictate the response of each subfield to conceptual traps, as defined
in the Science and Technology Studies literature. Finally, through a
comparative analysis of these currently siloed fields, we present a roadmap for
a unified approach to sociotechnical graduate pedagogy in AI.
