Machine-learning algorithms in regulatory practice

Nine organisational challenges for regulatory agencies

Authors

  • Lukas Lorenz Utrecht School of Governance, Utrecht University
  • Judith van Erp Utrecht School of Governance, Utrecht University
  • Albert Meijer Utrecht School of Governance, Utrecht University

DOI:

https://doi.org/10.26116/techreg.2022.001

Abstract

A growing body of literature discusses the impact of machine-learning algorithms on regulatory processes. This paper contributes to the predomi- nantly legal and technological literature by using a sociological-institutional perspective to identify nine organisational challenges for using algorithms in regulatory practice. Firstly, this paper identifies three forms of algorithms and regulation: regulation of algorithms, regulation through algorithms, and regulation of algorithms through algorithms. Secondly, we identify nine organisational challenges for regulation of and through algorithms based on literature analysis and empirical examples from Dutch regulatory agencies. Finally, we indicate what kind of institutional work regulatory agencies need to carry out to overcome the challenges and to develop an algorithmic regu- latory practice, which calls for future empirical research.

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Author Biographies

  • Lukas Lorenz, Utrecht School of Governance, Utrecht University

    Lukas Lorenz is a PhD candidate at the Utrecht School of Governance at Utrecht University, the Netherlands.

  • Judith van Erp, Utrecht School of Governance, Utrecht University

    Judith van Erp is professor of regulatory governance at the Utrecht School of Governance at Utrecht University, the Netherlands.

  • Albert Meijer, Utrecht School of Governance, Utrecht University

    Albert Meijer is professor of public innovation at the Utrecht School of Governance at Utrecht University, the Netherlands.

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Published

07-02-2022 — Updated on 11-02-2022

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How to Cite

Lorenz, L., van Erp, J., & Meijer, A. (2022). Machine-learning algorithms in regulatory practice: Nine organisational challenges for regulatory agencies. Technology and Regulation, 2022, 1-11. https://doi.org/10.26116/techreg.2022.001 (Original work published 2022)