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

Michael Veale, Max Van Kleek and Reuben Binns

No 8kvf4, SocArXiv from Center for Open Science

Abstract: Cite as: Michael Veale, Max Van Kleek and Reuben Binns (2018) Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. ACM Conference on Human Factors in Computing Systems (CHI'18). doi: 10.1145/3173574.3174014 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.

Date: 2018-02-04
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://osf.io/download/5a774f0b68ee49000f397947/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:8kvf4

DOI: 10.31219/osf.io/8kvf4

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().