Veale, M;
Brass, I;
(2019)
Administration by Algorithm? Public Management meets Public Sector Machine Learning.
In: Yeung, K and Lodge, M, (eds.)
Algorithmic Regulation.
(pp. 121-149).
Oxford University Press: Oxford, UK.
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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.
| Type: | Book chapter |
|---|---|
| Title: | Administration by Algorithm? Public Management meets Public Sector Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/oso/9780198838494.003.0006 |
| Publisher version: | https://doi.org/10.1093/oso/9780198838494.003.0006 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | machine learning systems, public sector, government, transformative effect, dynamic conservatism |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > STEaPP UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Laws |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10072507 |
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