Machine learning political orders

Amoore, Louise

Abstract

A significant set of epistemic and political transformations are taking place as states and societies begin to understand themselves and their problems through the paradigm of deep neural network algorithms. A machine learning political order does not merely change the political technologies of governance, but is itself a reordering of politics, of what the political can be. When algorithmic systems reduce the pluridimensionality of politics to the output of a model, they simultaneously foreclose the potential for other political claims to be made and alternative political projects to be built. More than this foreclosure, a machine learning political order actively profits and learns from the fracturing of communities and the destabilising of democratic rights. The transformation from rules-based algorithms to deep learning models has paralleled the undoing of rules-based social and international orders – from the use of machine learning in the campaigns of the UK EU referendum, to the trialling of algorithmic immigration and welfare systems, and the use of deep learning in the COVID-19 pandemic – with political problems becoming reconfigured as machine learning problems. Machine learning political orders decouple their attributes, features and clusters from underlying social values, no longer tethered to notions of good governance or a good society, but searching instead for the optimal function of abstract representations of data.

Citation

Amoore, L. (2023). Machine learning political orders. Review of International Studies, 49(1), 20-36. https://doi.org/10.1017/s0260210522000031

• Journal Article Type: Article
• Acceptance Date: Dec 17, 2021
• Online Publication Date: Feb 15, 2022
• Publication Date: 2023-01
• Deposit Date: May 25, 2022
• Publicly Available Date: Mar 10, 2023
• Journal: Review of International Studies
• Print ISSN: 0260-2105
• Electronic ISSN: 1469-9044
• Publisher: Cambridge University Press
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 1
• Pages: 20-36
• DOI: https://doi.org/10.1017/s0260210522000031
• Public URL: https://durham-repository.worktribe.com/output/1204485