text: 240,71,960872925560823810,20309837,Michael Veale,"We talk lots about theoretical algorithmic fairness+accountability, but can we learn from those grappling w/ these issues on the ground? 27 folks in police, justice, child welfare ML shared their experiences in our new #chi2018 paper <LINK> #fatml @emax @RDBinns <LINK>",https://arxiv.org/abs/1802.01029,"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. ","Fairness and Accountability Design Needs for Algorithmic Support in