Discriminating data : correlation, neighborhoods, and the new politics of recognition
"Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"--
Gespeichert in:
| Autor*in: |
Chun, Wendy Hui Kyong - 1969- [verfasserIn] Barnett, Alex - 1972 December 7- [illustratorIn] |
|---|
| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
Cambridge, Massachusetts: The MIT Press ; 2021 |
|---|
| Anmerkung: |
Includes bibliographical references and index. - Print version record |
|---|---|
| Umfang: |
1 online resource (xi, 327 pages) ; illustrations |
| Links: | |
|---|---|
| ISBN: |
0-262-36726-2 978-0-262-36726-4 978-0-262-36725-7 0-262-36725-4 |
| Katalog-ID: |
1786343401 |
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| 520 | |a 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. -- | ||
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0262367262 electronic bk. 0-262-36726-2 9780262367264 electronic bk. 978-0-262-36726-4 9780262367257 electronic bk. 978-0-262-36725-7 0262367254 electronic bk. 0-262-36725-4 (DE-627)1786343401 (DE-599)KEP072599316 (OCoLC)1285067195 (MITPRESS)9616972 (EBP)072599316 DE-627 ger DE-627 rda eng 005.7 23 Chun, Wendy Hui Kyong 1969- verfasserin aut Discriminating data correlation, neighborhoods, and the new politics of recognition Wendy Hui Kyong Chun ; mathematical illustrations by Alex Barnett Cambridge, Massachusetts The MIT Press [2021] 1 online resource (xi, 327 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Print version record "Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- 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. -- Big data Social aspects Artificial intelligence Social aspects Privacy, Right of Artificial intelligence ; Social aspects Big data ; Social aspects Privacy, Right of COMPUTERS / Data Science / Data Analytics SOCIAL SCIENCE / Sociology / Urban POLITICAL SCIENCE / Public Policy / Cultural Policy Données volumineuses ; Aspect social Intelligence artificielle ; Aspect social Electronic books Barnett, Alex 1972 December 7- illustratorin ill 9780262046220 Erscheint auch als Druck-Ausgabe 9780262046220 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 X:MITPRESS Verlag lizenzpflichtig https://ieeexplore.ieee.org/book/9616972 X:MITPRESS Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780262046220.pdf 2022-07-28 Aggregator Inhaltsverzeichnis ZDB-37-IEM 2021 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 BO 045F 005.7 22 01 0018 4046237775 olrm-h228-MITIEEE zi22818 27-01-22 23 01 0830 404623895X olr-MIT i z 27-01-22 62 01 0028 4046243031 OLR-MIT Vervielfältigungen (z.B. 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| spelling |
0262367262 electronic bk. 0-262-36726-2 9780262367264 electronic bk. 978-0-262-36726-4 9780262367257 electronic bk. 978-0-262-36725-7 0262367254 electronic bk. 0-262-36725-4 (DE-627)1786343401 (DE-599)KEP072599316 (OCoLC)1285067195 (MITPRESS)9616972 (EBP)072599316 DE-627 ger DE-627 rda eng 005.7 23 Chun, Wendy Hui Kyong 1969- verfasserin aut Discriminating data correlation, neighborhoods, and the new politics of recognition Wendy Hui Kyong Chun ; mathematical illustrations by Alex Barnett Cambridge, Massachusetts The MIT Press [2021] 1 online resource (xi, 327 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Print version record "Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- 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. -- Big data Social aspects Artificial intelligence Social aspects Privacy, Right of Artificial intelligence ; Social aspects Big data ; Social aspects Privacy, Right of COMPUTERS / Data Science / Data Analytics SOCIAL SCIENCE / Sociology / Urban POLITICAL SCIENCE / Public Policy / Cultural Policy Données volumineuses ; Aspect social Intelligence artificielle ; Aspect social Electronic books Barnett, Alex 1972 December 7- illustratorin ill 9780262046220 Erscheint auch als Druck-Ausgabe 9780262046220 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 X:MITPRESS Verlag lizenzpflichtig https://ieeexplore.ieee.org/book/9616972 X:MITPRESS Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780262046220.pdf 2022-07-28 Aggregator Inhaltsverzeichnis ZDB-37-IEM 2021 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 BO 045F 005.7 22 01 0018 4046237775 olrm-h228-MITIEEE zi22818 27-01-22 23 01 0830 404623895X olr-MIT i z 27-01-22 62 01 0028 4046243031 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 27-01-22 100 01 3100 4472470241 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4046237546 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 27-01-22 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/servlet/opac?bknumber=9616972 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 62 01 0028 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 100 01 3100 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/servlet/opac?bknumber=9616972 370 01 4370 E-Book: Zugriff im HCU-Netz. 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| allfields_unstemmed |
0262367262 electronic bk. 0-262-36726-2 9780262367264 electronic bk. 978-0-262-36726-4 9780262367257 electronic bk. 978-0-262-36725-7 0262367254 electronic bk. 0-262-36725-4 (DE-627)1786343401 (DE-599)KEP072599316 (OCoLC)1285067195 (MITPRESS)9616972 (EBP)072599316 DE-627 ger DE-627 rda eng 005.7 23 Chun, Wendy Hui Kyong 1969- verfasserin aut Discriminating data correlation, neighborhoods, and the new politics of recognition Wendy Hui Kyong Chun ; mathematical illustrations by Alex Barnett Cambridge, Massachusetts The MIT Press [2021] 1 online resource (xi, 327 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Print version record "Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- 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. -- Big data Social aspects Artificial intelligence Social aspects Privacy, Right of Artificial intelligence ; Social aspects Big data ; Social aspects Privacy, Right of COMPUTERS / Data Science / Data Analytics SOCIAL SCIENCE / Sociology / Urban POLITICAL SCIENCE / Public Policy / Cultural Policy Données volumineuses ; Aspect social Intelligence artificielle ; Aspect social Electronic books Barnett, Alex 1972 December 7- illustratorin ill 9780262046220 Erscheint auch als Druck-Ausgabe 9780262046220 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 X:MITPRESS Verlag lizenzpflichtig https://ieeexplore.ieee.org/book/9616972 X:MITPRESS Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780262046220.pdf 2022-07-28 Aggregator Inhaltsverzeichnis ZDB-37-IEM 2021 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 BO 045F 005.7 22 01 0018 4046237775 olrm-h228-MITIEEE zi22818 27-01-22 23 01 0830 404623895X olr-MIT i z 27-01-22 62 01 0028 4046243031 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 27-01-22 100 01 3100 4472470241 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4046237546 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 27-01-22 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/servlet/opac?bknumber=9616972 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 62 01 0028 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 100 01 3100 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/servlet/opac?bknumber=9616972 370 01 4370 E-Book: Zugriff im HCU-Netz. 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0262367262 electronic bk. 0-262-36726-2 9780262367264 electronic bk. 978-0-262-36726-4 9780262367257 electronic bk. 978-0-262-36725-7 0262367254 electronic bk. 0-262-36725-4 (DE-627)1786343401 (DE-599)KEP072599316 (OCoLC)1285067195 (MITPRESS)9616972 (EBP)072599316 DE-627 ger DE-627 rda eng 005.7 23 Chun, Wendy Hui Kyong 1969- verfasserin aut Discriminating data correlation, neighborhoods, and the new politics of recognition Wendy Hui Kyong Chun ; mathematical illustrations by Alex Barnett Cambridge, Massachusetts The MIT Press [2021] 1 online resource (xi, 327 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Print version record "Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- 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. -- Big data Social aspects Artificial intelligence Social aspects Privacy, Right of Artificial intelligence ; Social aspects Big data ; Social aspects Privacy, Right of COMPUTERS / Data Science / Data Analytics SOCIAL SCIENCE / Sociology / Urban POLITICAL SCIENCE / Public Policy / Cultural Policy Données volumineuses ; Aspect social Intelligence artificielle ; Aspect social Electronic books Barnett, Alex 1972 December 7- illustratorin ill 9780262046220 Erscheint auch als Druck-Ausgabe 9780262046220 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 X:MITPRESS Verlag lizenzpflichtig https://ieeexplore.ieee.org/book/9616972 X:MITPRESS Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780262046220.pdf 2022-07-28 Aggregator Inhaltsverzeichnis ZDB-37-IEM 2021 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 BO 045F 005.7 22 01 0018 4046237775 olrm-h228-MITIEEE zi22818 27-01-22 23 01 0830 404623895X olr-MIT i z 27-01-22 62 01 0028 4046243031 OLR-MIT Vervielfältigungen (z.B. 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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. -- Big data Social aspects Artificial intelligence Social aspects Privacy, Right of Artificial intelligence ; Social aspects Big data ; Social aspects Privacy, Right of COMPUTERS / Data Science / Data Analytics SOCIAL SCIENCE / Sociology / Urban POLITICAL SCIENCE / Public Policy / Cultural Policy Données volumineuses ; Aspect social Intelligence artificielle ; Aspect social Electronic books Barnett, Alex 1972 December 7- illustratorin ill 9780262046220 Erscheint auch als Druck-Ausgabe 9780262046220 https://ieeexplore.ieee.org/servlet/opac?bknumber=9616972 X:MITPRESS Verlag lizenzpflichtig https://ieeexplore.ieee.org/book/9616972 X:MITPRESS Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780262046220.pdf 2022-07-28 Aggregator Inhaltsverzeichnis ZDB-37-IEM 2021 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 BO 045F 005.7 22 01 0018 4046237775 olrm-h228-MITIEEE zi22818 27-01-22 23 01 0830 404623895X olr-MIT i z 27-01-22 62 01 0028 4046243031 OLR-MIT Vervielfältigungen (z.B. 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"Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- 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. -- Includes bibliographical references and index. - Print version record |
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"Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- 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. -- Includes bibliographical references and index. - Print version record |
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"Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- 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. -- Includes bibliographical references and index. - Print version record |
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Preface p. ix Introduction: How to Destroy the World, One Solution at a Time … Red Pill Toxicity, Or Liberation Envy … Correlating Eugenics … The Transgressive Hypothesis … Homophily, or the Swarming of the Segregated Neighborhood … Proxies, or Reconstructing the Unknown … Algorithmic Authenticity … Correlating Ideology, or What Lies at the Surface … Recognizing Recognition … The Space Between Us … Coda: Living in Difference … Acknowledgments … Notes … References for Mathematical Illustrations … Index … |
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