Data science, artificial intelligence and the third wave of digital era governance

Dunleavy, Patrick ORCID: 0000-0002-2650-6398 and Margetts, Helen (2023) Data science, artificial intelligence and the third wave of digital era governance. Public Policy and Administration. ISSN 0952-0767

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Abstract

This article examines the model of digital era governance (DEG) in the light of the latest-wave of data-driven technologies, such as data science methodologies and artificial intelligence (labelled here DSAI). It identifies four key top-level macro-themes through which digital changes in response to these developments may be investigated. First, the capability to store and analyse large quantities of digital data obviates the need for data ‘compression’ that characterises Weberian-model bureaucracies, and facilitates data de-compression in data-intensive information regimes, where the capabilities of public agencies and civil society are both enhanced. Second, the increasing capability of robotic devices have expanded the range of tasks that machines extending or substituting workers’ capabilities can perform, with implications for a reshaping of state organisation. Third, DSAI technologies allow new options for partitioning state functions in ways that can maximise organisational productivity, in an ‘intelligent centre, devolved delivery’ model within vertical policy sectors. Fourth, within each tier of government, DSAI technologies offer new possibilities for ‘administrative holism’ - the horizontal allocation of power and functions between organisations, through state integration, common capacity and needs-based joining-up of services. Together, these four themes comprise a third wave of DEG changes, suggesting important administrative choices to be made regarding information regimes, state organisation, functional allocation and outsourcing arrangements, as well as a long-term research agenda for public administration, requiring extensive and detailed analysis.

Item Type:	Article
Additional Information:	© 2023 The Author(s)
Divisions:	LSE
Subjects:	J Political Science
Date Deposited:	03 Oct 2023 11:54
Last Modified:	25 Apr 2024 17:30
URI:	http://eprints.lse.ac.uk/id/eprint/120352

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