Administration by Algorithm? Public Management Meets Public Sector Machine Learning

In: Algorithmic Regulation (Karen Yeung and Martin Lodge eds., Oxford University Press, 2019)

30 Pages Posted: 20 Apr 2019

See all articles by Michael Veale

Michael Veale

University College London, Faculty of Laws; Alan Turing Institute - Alan Turing Institute

Irina Brass

University College London - Department of Science, Technology, Engineering and Public Policy (STEaPP)

Date Written: 2019

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.

Keywords: algorithmic decision-making, machine learning, public management, public administration, e-government, automated decision-making

Suggested Citation

Veale, Michael and Brass, Irina, Administration by Algorithm? Public Management Meets Public Sector Machine Learning (2019). In: Algorithmic Regulation (Karen Yeung and Martin Lodge eds., Oxford University Press, 2019). Available at SSRN: https://ssrn.com/abstract=3375391

Michael Veale (Contact Author)

University College London, Faculty of Laws ( email )

Gower St
London WC1E OEG, WC1E 6BT
United Kingdom

Alan Turing Institute - Alan Turing Institute ( email )

96 Euston Road
London, NW1 2DB
United Kingdom

Irina Brass

University College London - Department of Science, Technology, Engineering and Public Policy (STEaPP) ( email )

Boston House
36–38 Fitzroy Square
London, W1t 3EY
United Kingdom

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