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Algorithmic Regulation$
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Karen Yeung and Martin Lodge

Print publication date: 2019

Print ISBN-13: 9780198838494

Published to Oxford Scholarship Online: October 2019

DOI: 10.1093/oso/9780198838494.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 24 September 2021

Administration by Algorithm?

Administration by Algorithm?

Public Management Meets Public Sector Machine Learning

(p.121) 6 Administration by Algorithm?
Algorithmic Regulation

Michael Veale

Irina Brass

Oxford University Press

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.

Keywords:   machine learning systems, public sector, government, transformative effect, dynamic conservatism

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