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

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 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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Research works printed but not linked: 14

  1. Carr N, 2015, The Glass Cage: Where Automation is Taking Us
  2. Cohen JE, 2014, Information, Freedom and Property: The Philosophy of Law Meets the Philosophy of Technology, 57
  3. Craig PP, 2012, Administrative Law
  4. Executive Office of the President, 2014, Big Data: Seizing Opportunities
  5. Executive Office of the President, 2016, Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights
  6. Feely M, 1994, The Futures of Criminology, 173
  7. Gandy OH, 1993, The Panoptic Sort
  8. Gillespie T, 2016, Critical Algorithm Studies: A Reading List, Social Media Collective Research Blog
  9. IliadisA RussoF(eds.) (2016)Big Data & Society Special Issue on Critical Data Studies. [Last accessed 11 Nov 2016.] Available from URL:http://bds.sagepub.com/content/critical‐data‐studies
  10. Mayer‐Schonberger V, 2013, Big Data
  11. Oliver D, 1994, Public Law, 238
  12. O'Reilly T, 2013, Beyond Transparency: Open Data and the Future of Civic Innovation, 289
  13. Williamson OE, 1975, Markets and Hierarchies: Analysis and Antitrust Implications: A Study in the Economics of Internal Organization
  14. World Economic Forum, 2011, Personal Data – The Emergence of a New Asset Class

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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
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
  • 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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