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

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  • Veale, Michael
  • Van Kleek, Max
  • Binns, Reuben

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.

Suggested Citation

  • Veale, Michael & Van Kleek, Max & Binns, Reuben, 2018. "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making," SocArXiv 8kvf4, Center for Open Science.
    • Handle: RePEc:osf:socarx:8kvf4
    DOI: 10.31219/osf.io/8kvf4
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    References listed on IDEAS

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    3. Veale, Michael & Binns, Reuben, 2017. "Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data," SocArXiv ustxg, Center for Open Science.
    4. Edwards, Lilian & Veale, Michael, 2017. "Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for," LawArXiv 97upg, Center for Open Science.
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    Cited by:

    1. Matus, Kira & Veale, Michael, 2021. "Certification Systems for Machine Learning: Lessons from Sustainability," SocArXiv pm3wy, Center for Open Science.
    2. Kuziemski, Maciej & Misuraca, Gianluca, 2020. "AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings," Telecommunications Policy, Elsevier, vol. 44(6).
    3. Vesnic-Alujevic, Lucia & Nascimento, Susana & Pólvora, Alexandre, 2020. "Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks," Telecommunications Policy, Elsevier, vol. 44(6).
    4. Veale, Michael & Brass, Irina, 2019. "Administration by Algorithm? Public Management meets Public Sector Machine Learning," SocArXiv mwhnb, Center for Open Science.
    5. Kathrin Hartmann & Georg Wenzelburger, 2021. "Uncertainty, risk and the use of algorithms in policy decisions: a case study on criminal justice in the USA," Policy Sciences, Springer;Society of Policy Sciences, vol. 54(2), pages 269-287, June.
    6. Fatima, Samar & Desouza, Kevin C. & Dawson, Gregory S., 2020. "National strategic artificial intelligence plans: A multi-dimensional analysis," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 178-194.
    7. Kolkman, Daan, 2020. "The usefulness of algorithmic models in policy making," SocArXiv hpma8, Center for Open Science.

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