Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18) doi:10.1145/3173574.3174014, ISBN: 978-1-4503-5620-6

14 Pages Posted: 23 May 2018

See all articles by Michael Veale

Michael Veale

Alan Turing Institute - Alan Turing Institute; University of Birmingham - Birmingham Law School

Max Van Kleek

University of Oxford - Computing Laboratory

Reuben Binns

University of Oxford

Date Written: April 21, 2018

Abstract

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.

Keywords: machine learning, algorithmic accountability, accountability, fairness, public sector, decision-support, policing, child welfare, taxation

Suggested Citation

Veale, Michael and Van Kleek, Max and Binns, Reuben, Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (April 21, 2018). Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18) doi:10.1145/3173574.3174014, ISBN: 978-1-4503-5620-6. Available at SSRN: https://ssrn.com/abstract=3175424

Michael Veale (Contact Author)

Alan Turing Institute - Alan Turing Institute ( email )

96 Euston Road
London, NW1 2DB
United Kingdom

University of Birmingham - Birmingham Law School ( email )

Edgbaston
Birmingham, B15 2TT
United Kingdom

Max Van Kleek

University of Oxford - Computing Laboratory ( email )

Wolfson Building
Parks Road
Oxford, OX1 3QD
United Kingdom

Reuben Binns

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

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