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Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recogni
US $8.92
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Condition:
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Item specifics
- Condition
- ISBN
- 9780262046220
About this product
Product Identifiers
Publisher
MIT Press
ISBN-10
0262046229
ISBN-13
9780262046220
eBay Product ID (ePID)
11050057356
Product Key Features
Number of Pages
344 Pages
Language
English
Publication Name
Discriminating Data : Correlation, Neighborhoods, and the New Politics of Recognition
Subject
Social Aspects, Discrimination & Race Relations, General
Publication Year
2021
Type
Textbook
Subject Area
Mathematics, Technology & Engineering, Social Science
Format
Hardcover
Dimensions
Item Height
1.3 in
Item Weight
21.7 Oz
Item Length
9.3 in
Item Width
6.5 in
Additional Product Features
Intended Audience
Trade
LCCN
2021-000481
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
005.7
Table Of Content
Preface ix Introduction: How to Destroy the World, One Solution at a Time 1 Red Pill Toxicity, or Liberation Envy 29 1 Correlating Eugenics 35 The Transgressive Hypothesis 75 2 Homophily, or the Swarming of the Segregated Neighborhood 81 3 Algorithmic Authenticity 139 Correlating Ideology, or What Lies at the Surface 173 4 Recognizing Recognition 185 The Space Between Us 231 Coda: Living in Difference 239 Acknowledgments 255 Notes 259 References for Mathematical Illustrations 317 Index 319
Synopsis
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. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates-groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data., 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. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates--groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.
Illustrated by
Barnett, Alex
LC Classification Number
QA76.9.B45C57 2021
Item description from the seller
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