Machine learning political orders.
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 gove...
| Publicado en: | Review of International Studies Vol. 49; no. 1; pp. 20 - 37 |
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| Formato: | Artículo |
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Cambridge University Press
Jan2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=160650972&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 160650972 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02602105 7YD jtl: Review of International Studies issn: 02602105 maglogo: N pubinfo: dt: Jan2023 vid: 49 iid: 1 pid: 15979 pub: Cambridge University Press artinfo: ui: 160650972 10.1017/S0260210522000031 ppf: 20 ppct: 17 formats: tig: atl: Machine learning political orders. aug: au: Amoore, Louise affil: Department of Geography, Durham University, Durham, United Kingdom su: Machine learning Emigration & immigration Deep learning Brexit Referendum, 2016 Communities International organization United Kingdom sug: subj: United Kingdom Machine learning Emigration & immigration Deep learning Brexit Referendum, 2016 Communities International organization keyword: Algorithm International Order Machine Learning Politics Rules ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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