Algorithmic War: Everyday Geographies of the War on Terror
Open accessAbstract
Technologies that deploy algorithmic calculation are becoming ubiquitous to the homeland securitization of the war on terror. From the surveillance networks of the city subway to the biometric identifiers of new forms of border control, the possibility to identify “association rules” between people, places, objects and events has brought the logic of pre‐emption into the most mundane and prosaic spaces. Yet, it is not the case that the turn to algorithmic calculation simplymilitarizessociety, nor even that we are witnessing strictly acommercializationof security. Rather, algorithmic war is one form of Foucault's sense of a “continuation of war by other means”, where the war‐like architectures of self/other, here/there, safe/risky, normal/suspicious are played out in the politics of daily life. This paper explores the situated interplay of algorithmic practices across commercial, security, and military spheres, revealing the violent geographies that are concealed in the glossy techno‐science of algorithmic calculation.
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Big data algorithms and data mining are fundamentally about discrimination; their purpose is to separate society into groups through identifying patterns of difference and sameness (norms and deviations) in vast datasets through data reduction and categorization.
“Big data algorithms and data mining are fundamentally about discrimination (see Barocas 2014); their purpose is to separate society into groups through identifying patterns of difference and sameness-or norms and deviations from them (Amoore 2009)-in vast datasets through processes of data reduction and categorization.”
From Social Injustice in Surveillance Capitalism (2017) · cites this paper as Amoore 2009
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Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
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Social Injustice in Surveillance Capitalism
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Manky, O. (2026) · New Technology, Work and Employment · doi:10.1111/ntwe.70005 · Cited by 1 · see citing works
- Authors
- Amoore, L.
- Affiliations
- Durham University (GB)
- Corresponding author
- Amoore, Louise
- ORCID
- Amoore, Louise
- Venue
- Antipode
- Publisher
- Wiley
- Published
- 2009-01
- Volume
- 41
- Issue
- 1
- Pages
- 49-69
- ISSN
- 0066-4812 (ISSN-L)
- 1467-8330
- OpenAlex
- W2014717813
- Cited by
- 410
- References
- 1
- Citations by year
- 2026: 22 · 2025: 40 · 2024: 19 · 2023: 21 · 2022: 30 · 2021: 39 · 2020: 52 · 2019: 31 · 2018: 33 · 2017: 13 · 2016: 21 · 2015: 20 · 2014: 17 · 2013: 20 · 2012: 8
- FWCI
- 27.99
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Antipode (publishedVersion)
- Durham Research Online (Durham University) (acceptedVersion)
- Durham Research Online (Durham University) (acceptedVersion, other-oa)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- Funders
- University of Edinburgh
- Economic and Social Research Council
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Terrorism, Counterterrorism, and Political Violence
- Social Sciences › Communication › Cultural Studies and Postmodernism
- Keywords
- war on terror, homeland security, surveillance, militarization, Michel Foucault, everyday life, self-other boundary, association rule mining
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