Название: Discriminating data: Correlation, Neighborhoods, and the New Politics of Recognition
Автор: Wendy Hui Kyong Chun, Alex Barnett
Издательство: The MIT Press
Год: 2021
Страниц: 344
Язык: английский
Формат: epub
Размер: 10.1 MB
How Big Data and Machine Learning encode discrimination and create agitated clusters of comforting rage.
In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within Big Data and Machine Learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds Big Data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine Learning and data analytics thus seek to disrupt the future by making disruption impossible.
Автор: Wendy Hui Kyong Chun, Alex Barnett
Издательство: The MIT Press
Год: 2021
Страниц: 344
Язык: английский
Формат: epub
Размер: 10.1 MB
How Big Data and Machine Learning encode discrimination and create agitated clusters of comforting rage.
In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within Big Data and Machine Learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds Big Data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine Learning and data analytics thus seek to disrupt the future by making disruption impossible.
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