AutorIn 1:
Veale, Michael
AutorIn 2:
Van Kleek, Max
AutorIn 3:
Binns, Reuben
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
"(...) 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. (...)"
[Computers and Society; Public Policy Issues; Models and Principles; User/Machine Systems; Computer Applications; Administrative Data Processing; algorithmic accountability; algorithmic bias; public administration; predictive policing; decision-support]
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