Voorbij de system-level bureaucratie: Over datastromen, algoritmes en inclusieve AI in de databureaucratie

Marc Schuilenburg, Rik Peeters

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Around the turn of the century, government digitalization mostly consisted of the automation of relatively simple bureaucratic procedures and the digitalization of organizations’ client databases. In this article, we argue that the ‘system-level bureaucracy’ has been surpassed by the emergence of information infrastructures, in which (big) data is shared among a wide variety of organizations, and the increased use of machine learning algorithms to assist administrative decision-making. We call this the ‘data-bureaucracy’. Rather than mere technical innovations developed to improve government efficiency, these developments have profound consequences for the way government organizations use public and private data, organize decision-making processes, and can be held accountable for their actions and decisions by citizens. We speak of the emergence of a ‘coding elite’ – data professionals that design concrete AI-applications and, in doing so, make (implicit) trade-offs between relevant public values beyond political and public scrutiny. In order to recover public value deliberation in algorithmic governance, we argue for the importance of inclusive design processes of AI-applications and develop a concrete framework for realizing such processes (‘inclusive AI’).
Original languageDutch
Article numberBENM 2024/3
Pages (from-to)278-293
JournalBeleid & Maatschappij
Volume51
Issue number3
DOIs
Publication statusPublished - 1 Dec 2024

Research programs

  • SAI 2005-04 MSS

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