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Soul of a new machine: Self-learning algorithms in public administration

Soul of a new machine: Self-learning algorithms in public administration Big data sets in conjunction with self-learning algorithms are becoming increasingly important in public administration. A growing body of literature demonstrates that the use of such technologies poses fundamental questions about the way in which predictions are generated, and the extent to which such predictions may be used in policy making. Complementing other recent works, the goal of this article is to open the machine’s black box to understand and critically examine how self-learning algorithms gain agency by transforming raw data into policy recommendations that are then used by policy makers. I identify five major concerns and discuss the implications for policy making. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Information Polity IOS Press

Soul of a new machine: Self-learning algorithms in public administration

Gerrits, Lasse
Information Polity , Volume 26 (3): 14 – Aug 17, 2021
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Publisher
IOS Press
Copyright
Copyright © 2021 © 2021 – The authors. Published by IOS Press.
ISSN
1570-1255
eISSN
1875-8754
DOI
10.3233/IP-200224
Publisher site
See Article on Publisher Site

Abstract

Big data sets in conjunction with self-learning algorithms are becoming increasingly important in public administration. A growing body of literature demonstrates that the use of such technologies poses fundamental questions about the way in which predictions are generated, and the extent to which such predictions may be used in policy making. Complementing other recent works, the goal of this article is to open the machine’s black box to understand and critically examine how self-learning algorithms gain agency by transforming raw data into policy recommendations that are then used by policy makers. I identify five major concerns and discuss the implications for policy making.

Journal

Information Polity – IOS Press

Published: Aug 17, 2021

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