  Calls for heightened consideration of fairness and accountability in
algorithmically-informed public decisions---like taxation, justice, and child
protection---are now commonplace. How might designers support such human
values? We interviewed 27 public sector machine learning practitioners across 5
OECD countries regarding challenges understanding and imbuing public values
into their work. The results suggest a disconnect between organisational and
institutional realities, constraints and needs, and those addressed by current
research into usable, transparent and 'discrimination-aware' machine
learning---absences likely to undermine practical initiatives unless addressed.
We see design opportunities in this disconnect, such as in supporting the
tracking of concept drift in secondary data sources, and in building usable
transparency tools to identify risks and incorporate domain knowledge, aimed
both at managers and at the 'street-level bureaucrats' on the frontlines of
public service. We conclude by outlining ethical challenges and future
directions for collaboration in these high-stakes applications.
