Hui chun (Plus de 4 200 résultats)

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Hardcover. Etat : Fair. With dust jacket. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Paperback. Etat : New. Paperback. Publisher overstock, may contain remainder mark on edge. Barnett, Alex (illustrateur).

Trade paperback. Etat : Very good. Trade paperback (US). Glued binding. 432 p. Contains: Unspecified, Illustrations, black & white, Halftones, black & white. Audience: General/trade.

Etat : Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Etat : Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Etat : Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Etat : good. Fast & Free Shipping â" Good condition. It may show normal signs of use, such as light writing, highlighting, or library markings, but all pages are intact and the book is fully readable. A solid, complete copy that's ready to enjoy.

Etat : good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

Paperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

paperback. Etat : New. Barnett, Alex (illustrateur).

Etat : New.

Etat : As New. Unread book in perfect condition.

paperback. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Paperback. Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. Barnett, Alex (illustrateur).

Paperback. Etat : New. 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. Barnett, Alex (illustrateur).…

Etat : New.

paperback. Etat : Good. Bruise/tear to cover.

Paperback. Etat : new. Paperback. How big data and machine learning encode discrimination and create agitated clusters of comforting rage.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. "Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Barnett, Alex (illustrateur).…

hardcover. Etat : Very Good. hardcover great condition jm 6/1.

Etat : New.

paperback. Etat : New.

Etat : Good. paperback 100% of proceeds go to charity! Good condition with all pages in tact. Item shows signs of use and may have cosmetic defects.

Etat : very_good.

paperback. Etat : Very Good.

paperback. Etat : Fine.

Etat : Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Etat : very_good. Fast Free Shipping â" Very Good condition book with a firm cover and clean pages. Shows normal use and some light wear or limited notes markings. A solid, nice copy to enjoy.