We use a few cookies

Essential cookies keep the library working. With your permission, we'd also like to use analytics to see what's useful and improve the experience. Read our Privacy Policy or Cookie Policy.

Skip to main content
Home/e-Resources/Mathematics/Machine-learning algorithms in regulatory practice
PeriodicalsMathematics

Machine-learning algorithms in regulatory practice

Checking your access to this record.

Abstract & Overview

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.

Cite this record

Lukas Lorenz, Judith van Erp, Albert Jacob Meijer (2022). Machine-learning algorithms in regulatory practice. Technology and Regulation, 2022, 1–11. https://doi.org/10.71265/hznjkw26

Copy in another style

Lorenz, L., Erp, J. V., & Meijer, A. J. (2022). Machine-learning algorithms in regulatory practice. Technology and Regulation, 2022, 1–11. https://doi.org/10.71265/hznjkw26
Author names are used as deposited, so check their order before you submit.

Metadata is harvested from open-access indexes and can be incomplete. Check a citation against the published version before relying on it — see our open access & attribution policy.