Digitizing Surveillance: Categorization, Space, Inequality
Open accessAbstract
In this article, we seek to add to current debates about surveillance and society by critically exploring the social implications of a new and emerging raft of surveillance practices: those that specifically surround digital techniques and technologies. The article has four parts. In the first, we outline the nature of digital surveillance and consider how it differs from other forms of surveillance. The second part of the article explores the interconnections between digital techniques and the changing political economies of cities and urban societies. Here we explore the essential ambivalence of digital surveillance within the context of wider trends towards privatization, liberalization and social polarization. The third part provides some insights into particular aspects of digital surveillance through three examples: algorithmic video surveillance (in which closed circuit television systems are linked to software for the recognition of movement or identity); the increasingly prevalent practices of digital prioritization in transport and communications; and the medical surveillance of populations, wherein databases are created for increasingly mixed state and commercial medical purposes. Following this, in part four, we reflect on the policy and research implications raised by the spread of digital surveillance.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
When personal data are algorithmically processed to predict future behaviours and events, the people represented by the data are subjected to the material effects of discriminatory or inaccurate classification.
“When personal data are algorithmically processed to predict future behaviours and events, the people represented by the data are therefore subjected to the material effects of discriminatory or inaccurate classification (Graham and Wood 2003;Lyon 2003b).”
From Social Injustice in Surveillance Capitalism (2017) · cites this paper as Graham and Wood 2003
The citation lists haven’t been fetched yet
References 36 · Cited by 294
- Authors
- Graham, S., & Wood, D.
- Affiliations
- Newcastle University (GB)
- Venue
- Critical Social Policy
- Publisher
- SAGE Publications
- Published
- 2003-05
- Volume
- 23
- Issue
- 2
- Pages
- 227-248
- ISSN
- 0261-0183 (ISSN-L)
- 1461-703X
- OpenAlex
- W2102395943
- Cited by
- 294
- References
- 36
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 12 · 2023: 13 · 2022: 27 · 2021: 14 · 2020: 39 · 2019: 20 · 2018: 7 · 2017: 16 · 2016: 13 · 2015: 12 · 2014: 11 · 2013: 18 · 2012: 13
- FWCI
- 40.14
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Critical Social Policy (publishedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Topics
- Social Sciences › Communication › Cultural Studies and Postmodernism
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- digital surveillance, closed-circuit television, face recognition, urban political economy, privatization, social inequality, medical surveillance, health databases, social polarization, surveillance policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review