The DOI matches its Crossref record; 92 of the 93 references it cites were found in a registry or an index.
Algorithmic regulation: A critical interrogation
Open accessAbstract
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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In the citing paper
“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)
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“However, beyond this, critics of current research [50,193,266,286,287,297,297,306] argue that eforts will fail unless contextual, sociocultural, and social policy challenges are better understood.”
From Fairness in machine learning: A survey (2024) · cites this paper as [306]
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Cited by
Sorted by citations
Accountable Artificial Intelligence: Holding Algorithms to Account
Busuioc, M. (2021) · Public Administration Review · doi:10.1111/puar.13293 · Cited by 652
“…important concerns about meaningful oversight and accountability (Bullock 2019; Diakopoulos 2014; European Parliament Study 2019; Pasquale 2015; Yeung 2018; Yeung and Lodge 2019; Young, Bullock, and Lecy 2019) and the need to identify and diagnose where the potential for accountability…”
BackgroundFairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
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AI governance: themes, knowledge gaps and future agendas
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Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation
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Metadata
- 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
- 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
- green
- papers.ssrn.com
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Public Administration › 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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