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Cited by 105

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Administration by Algorithm? Public Management Meets Public Sector Machine Learning

Veale, Michael, Brass, Irina·2019·Algorithmic Regulation
DOI 10.1093/oso/9780198838494.003.0006
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Abstract

This chapter first explains the types of machine learning systems used in the public sector, detailing the processes and tasks that they aim to support. It then looks at three levels of government—the macro, meso, and the street-level—to map out, analyse, and evaluate how machine learning in the public sector more broadly is framed and standardized across government. It concludes that, while the use of machine learning in the public sector is mostly discussed with regard to its ‘transformative effect’ versus ‘the dynamic conservatism’ characteristic of public bureaucracies that embrace new technological developments, it also raises several concerns about the skills, capacities, processes, and practices that governments currently employ, the forms of which can have value-laden, political consequences.

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Metadata

Authors
Veale, M., & Brass, I.
Venue
Algorithmic Regulation
Publisher
Oxford University PressOxford
Published
2019-09-05
Pages
121-149
DOI
10.1093/oso/9780198838494.003.0006
Cited by
105
References
30
Language
en

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Veale, M., & Brass, I. (2019). Administration by Algorithm? Public Management Meets Public Sector Machine Learning. Algorithmic Regulation. https://doi.org/10.1093/oso/9780198838494.003.0006

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