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Cited by 983Digitalization, Law, and RegulationEthics and Social Impacts of AIRegulation and Compliance Studies

Algorithmic regulation: A critical interrogation

Open access
Yeung, Karen·2018·Regulation & Governance
DOI 10.1111/rego.12158
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Abstract

Innovations in networked digital communications technologies, including the rise of “Big Data,” ubiquitous computing, and cloud storage systems, may be giving rise to a new system of social ordering known as algorithmic regulation. Algorithmic regulation refers to decisionmaking systems that regulate a domain of activity in order to manage risk or alter behavior through continual computational generation of knowledge by systematically collecting data (in real time on a continuous basis) emitted directly from numerous dynamic components pertaining to the regulated environment in order to identify and, if necessary, automatically refine (or prompt refinement of) the system's operations to attain a pre‐specified goal. This study provides a descriptive analysis of algorithmic regulation, classifying these decisionmaking systems as either reactive or pre‐emptive, and offers a taxonomy that identifies eight different forms of algorithmic regulation based on their configuration at each of the three stages of the cybernetic process: notably, at the level of standard setting (adaptive vs. fixed behavioral standards), information‐gathering and monitoring (historic data vs. predictions based on inferred data), and at the level of sanction and behavioral change (automatic execution vs. recommender systems). It maps the contours of several emerging debates surrounding algorithmic regulation, drawing upon insights from regulatory governance studies, legal critiques, surveillance studies, and critical data studies to highlight various concerns about the legitimacy of algorithmic regulation.

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Crossref · 10.1111/rego.12158

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93 of 93 references verified0 citation errors · 0 not checkable

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  • Yeung (2018) conceptualises algorithmic regulation as a mode of governance that uses computational systems to monitor, evaluate and shape behaviour.

    “Yeung (2018) conceptualises algorithmic regulation as a mode of governance that uses computational systems to monitor, evaluate and shape behaviour.”

    From From Human Oversight to Cognitive Sovereignty: A Process-Based Governance Standard for AI-Assisted Legal Reasoning (2026) · cites this paper as Yeung (2018)

  • Without the opportunity to understand the basis upon which assessments are made, it becomes practically impossible for an individual to assert a claim of right or to protest that she is not like that, or that while she might have been like that in the past, she does not propose to be like that in the future.

    “Without the “opportunity to understand the basis upon which assessments are made” it becomes “practically impossible for any individual to assert a claim of right, to protest that ‘she is not like that’, or that while she might have been like that in the past, she does not propose to be ‘like that’ in the future” (Yeung 2018, 515).”

    From Accountable Artificial Intelligence: Holding Algorithms to Account (2021) · cites this paper as Yeung 2018

  • If left unaddressed, these blindnesses auger a future where the integration of AI into every nook and cranny of our societies will make it impossible to escape state control and surveillance capitalism.

    “make it impossible to escape state control and surveillance capitalism (Yeung 2018;Zuboff 2020)”

    From Abundant intelligences: placing AI within Indigenous knowledge frameworks (2025) · cites this paper as Yeung 2018

  • Many variables are explicitly defined as 'sensitive' by specific legal frameworks.

    “We note that many variables are explicitly deined as łsensitivež by speciic legal frameworks, see [25,129,130,191,192,209,268,280,306] and the references therein.”

    From Fairness in machine learning: A survey (2024) · cites this paper as [306]

  • A lack of consideration for the sociocultural context of the application can result in ML solutions that are biased, unethical, unfair, and often not legally permissible.

    “A lack of consideration for the sociocultural context of the application can result in ML solutions that are biased, unethical, unfair, and often not legally permissible [37,306].”

    From Fairness in machine learning: A survey (2024) · cites this paper as [306]

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Authors
Yeung, K.
Affiliations
The University of Melbourne (AU)
Corresponding author
Yeung, Karen
ORCID
Yeung, Karen
Venue
Regulation & Governance
Publisher
Wiley
Published
2017-07-31
Volume
12
Issue
4
Pages
505-523
ISSN
  • 1748-5983 (ISSN-L)
  • 1748-5991
DOI
10.1111/rego.12158
OpenAlex
W2619503097
Cited by
842
References
93
Citations by year
2026: 313 · 2025: 134 · 2024: 56 · 2023: 93 · 2022: 71 · 2021: 61 · 2020: 60 · 2019: 42 · 2018: 11 · 2017: 1
FWCI
321.68
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Regulation & Governance (publishedVersion)
  • SSRN Electronic Journal (submittedVersion)
  • University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Topics
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Regulation and Compliance Studies
Keywords
algorithmic regulation, regulatory governance, predictive policing, surveillance, recommender systems, regulatory legitimacy, big data, cloud computing, ubiquitous computing

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