  Formulating data science problems is an uncertain and difficult process. It
requires various forms of discretionary work to translate high-level objectives
or strategic goals into tractable problems, necessitating, among other things,
the identification of appropriate target variables and proxies. While these
choices are rarely self-evident, normative assessments of data science projects
often take them for granted, even though different translations can raise
profoundly different ethical concerns. Whether we consider a data science
project fair often has as much to do with the formulation of the problem as any
property of the resulting model. Building on six months of ethnographic
fieldwork with a corporate data science team---and channeling ideas from
sociology and history of science, critical data studies, and early writing on
knowledge discovery in databases---we describe the complex set of actors and
activities involved in problem formulation. Our research demonstrates that the
specification and operationalization of the problem are always negotiated and
elastic, and rarely worked out with explicit normative considerations in mind.
In so doing, we show that careful accounts of everyday data science work can
help us better understand how and why data science problems are posed in
certain ways---and why specific formulations prevail in practice, even in the
face of what might seem like normatively preferable alternatives. We conclude
by discussing the implications of our findings, arguing that effective
normative interventions will require attending to the practical work of problem
formulation.
