Algorithmic Governance and the International Politics of Big Tech

Big technology companies like Facebook, Google, and Amazon amass global power through classification algorithms. These algorithms use unsupervised and semi-supervised machine learning on massive databases to detect objects, such as faces, and to process texts, such as speech, to model predictions fo...

Full description

Saved in:
Bibliographic Details
Published inPerspectives on politics Vol. 21; no. 3; pp. 989 - 1000
Main Author Srivastava, Swati
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 01.09.2023
Subjects
Algorithms
Artificial intelligence
Automation
Classification
Decision making
Discrimination
Extremism
Governance
Interdisciplinary aspects
International relations
Politics
Pollution
Pollution control
Privacy
Reflection
Scrutiny
Smartphones
Social networks
Speech
Surveillance
United States > US
Online AccessGet full text

Cover

More Information
Summary:Big technology companies like Facebook, Google, and Amazon amass global power through classification algorithms. These algorithms use unsupervised and semi-supervised machine learning on massive databases to detect objects, such as faces, and to process texts, such as speech, to model predictions for commercial and political purposes. Such governance by algorithms—or “algorithmic governance”—has received critical scrutiny from a vast interdisciplinary scholarship that points to algorithmic harms related to mass surveillance, information pollution, behavioral herding, bias, and discrimination. Big Tech’s algorithmic governance implicates core IR research in two ways: (1) it creates new private authorities as corporations control critical bottlenecks of knowledge, connection, and desire; and (2) it mediates the scope of state–corporate relations as states become dependent on Big Tech, Big Tech circumvents state overreach, and states curtail Big Tech. As such, IR scholars should become more involved in the global research on algorithmic governance.
ISSN:1537-5927
1541-0986
DOI:10.1017/S1537592721003145