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PreprintCited by 316E-Government and Public ServicesEthics and Social Impacts of AIPublic Policy and Administration Research

Administration by Algorithm? Public Management meets Public Sector Machine Learning

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Veale, Michael, Brass, Irina·2019·Oxford University Press eBooks
DOI 10.31235/osf.io/mwhnb
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Abstract

Public bodies and agencies increasingly seek to use new forms of data analysis in order to provide 'better public services'. These reforms have consisted of digital service transformations generally aimed at 'improving the experience of the citizen', 'making government more efficient' and 'boosting business and the wider economy'. More recently however, there has been a push to use administrative data to build algorithmic models, often using machine learning, to help make day-to-day operational decisions in the management and delivery of public services rather than providing general policy evidence. This chapter asks several questions relating to this. What are the drivers of these new approaches? Is public sector machine learning a smooth continuation of e-Government, or does it pose fundamentally different challenge to practices of public administration? And how are public management decisions and practices at different levels enacted when machine learning solutions are implemented in the public sector? Focussing on different levels of government: the macro, the meso, and the 'street-level', we map out and analyse the current efforts to frame and standardise machine learning in the public sector, noting that they raise several concerns around the skills, capacities, processes and practices governments currently employ. The forms of these are likely to have value-laden, political consequences worthy of significant scholarly attention.

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Cited by

Sorted by citations

Authors
Veale, M., & Brass, I.
ORCID
Venue
Oxford University Press eBooks
Publisher
Center for Open Science
Published
2019-04-20
Pages
121-149
DOI
10.31235/osf.io/mwhnb
OpenAlex
W4244691365
Cited by
316
References
14
Citations by year
2026: 57 · 2025: 45 · 2024: 33 · 2023: 102 · 2022: 29 · 2021: 30 · 2020: 17 · 2019: 3
FWCI
192.17
Type
book chapter
Language
en
Open access
Locations
  • Oxford University Press eBooks (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • SocArXiv (OSF Preprints) (submittedVersion)
  • Oxford University Press eBooks
Funders
Engineering and Physical Sciences Research Council (N02334X/1, EP/M507970/1, EP/N02334X/1, M507970)
Topics
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Public Administration › Public Policy and Administration Research
Keywords
algorithmic governance, public administration, levels of government, street-level bureaucracy, digitalization of public administration

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