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The production of prediction: what does machine learning want?

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The production of prediction: what does machine learning want? / Mackenzie, Adrian.
In: European Journal of Cultural Studies, Vol. 18, No. 4-5, 08.2015, p. 429-445.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Mackenzie, A 2015, 'The production of prediction: what does machine learning want?', European Journal of Cultural Studies, vol. 18, no. 4-5, pp. 429-445. https://doi.org/10.1177/1367549415577384

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Mackenzie A. The production of prediction: what does machine learning want? European Journal of Cultural Studies. 2015 Aug;18(4-5):429-445. doi: 10.1177/1367549415577384

Author

Mackenzie, Adrian. / The production of prediction : what does machine learning want?. In: European Journal of Cultural Studies. 2015 ; Vol. 18, No. 4-5. pp. 429-445.

Bibtex

@article{8c9a264bb307498f95e76e13f97c40a4,
title = "The production of prediction: what does machine learning want?",
abstract = "Retail, media, finance, science, industry, security and government increasingly depend on predictions produced through techniques such as machine learning. How is it that machine learning can promise to predict with great specificity what differences matter or what people want in many different settings? We need, I suggest, an account of its generalization if we are to understand the contemporary production of prediction. This article maps the principal forms of material action, narrative and problematization that run across algorithmic modelling techniques such as logistic regression, decision trees and Naive Bayes classifiers. It highlights several interlinked modes of generalization that engender increasingly vast data infrastructures and platforms, and intensified mathematical and statistical treatments of differences. Such an account also points to some key sites of instability or problematization inherent to the process of generalization. If movement through data is becoming a principal intersection of power relations, economic value and valid knowledge, an account of the production of prediction might also help us begin to ask how its generalization potentially gives rise to new forms of agency, experience or individuations.",
keywords = "Knowledge, machine learning, media, power, prediction",
author = "Adrian Mackenzie",
year = "2015",
month = aug,
doi = "10.1177/1367549415577384",
language = "English",
volume = "18",
pages = "429--445",
journal = "European Journal of Cultural Studies",
issn = "1367-5494",
publisher = "SAGE Publications Ltd",
number = "4-5",

}

RIS

TY - JOUR

T1 - The production of prediction

T2 - what does machine learning want?

AU - Mackenzie, Adrian

PY - 2015/8

Y1 - 2015/8

N2 - Retail, media, finance, science, industry, security and government increasingly depend on predictions produced through techniques such as machine learning. How is it that machine learning can promise to predict with great specificity what differences matter or what people want in many different settings? We need, I suggest, an account of its generalization if we are to understand the contemporary production of prediction. This article maps the principal forms of material action, narrative and problematization that run across algorithmic modelling techniques such as logistic regression, decision trees and Naive Bayes classifiers. It highlights several interlinked modes of generalization that engender increasingly vast data infrastructures and platforms, and intensified mathematical and statistical treatments of differences. Such an account also points to some key sites of instability or problematization inherent to the process of generalization. If movement through data is becoming a principal intersection of power relations, economic value and valid knowledge, an account of the production of prediction might also help us begin to ask how its generalization potentially gives rise to new forms of agency, experience or individuations.

AB - Retail, media, finance, science, industry, security and government increasingly depend on predictions produced through techniques such as machine learning. How is it that machine learning can promise to predict with great specificity what differences matter or what people want in many different settings? We need, I suggest, an account of its generalization if we are to understand the contemporary production of prediction. This article maps the principal forms of material action, narrative and problematization that run across algorithmic modelling techniques such as logistic regression, decision trees and Naive Bayes classifiers. It highlights several interlinked modes of generalization that engender increasingly vast data infrastructures and platforms, and intensified mathematical and statistical treatments of differences. Such an account also points to some key sites of instability or problematization inherent to the process of generalization. If movement through data is becoming a principal intersection of power relations, economic value and valid knowledge, an account of the production of prediction might also help us begin to ask how its generalization potentially gives rise to new forms of agency, experience or individuations.

KW - Knowledge

KW - machine learning

KW - media

KW - power

KW - prediction

U2 - 10.1177/1367549415577384

DO - 10.1177/1367549415577384

M3 - Journal article

VL - 18

SP - 429

EP - 445

JO - European Journal of Cultural Studies

JF - European Journal of Cultural Studies

SN - 1367-5494

IS - 4-5

ER -