Chun Alex (104 results)

paperback. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Paperback. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

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

Paperback. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Hard Cover. Condition: vg+. Dust Jacket Condition: vg-. 1st ptg. 282 pages; yellow dust jacket has some rippling. Alex Camlin, dj il (illustrator). Hardcover (dj).

Soft cover. Condition: Fine. The book has some minor surface wear.

paperback. Condition: Fine. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages. Barnett, Alex (illustrator).

Condition: good. The book is in good condition with all pages and cover intact, including the dust jacket if originally issued. The spine may show light wear. Pages may contain some notes or highlighting, and there might be a "From the library of" label. Boxed set packaging, shrink wrap, or included media like CDs may be missing. Barnett, Alex (illustrator).…

Condition: New. Barnett, Alex (illustrator).

Paperback. Condition: 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 (illustrator).…

Paperback. Condition: 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 (illustrator).…

Hardback. Condition: Very Good. 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 (illustrator). …

Hardback. Condition: Very Good. 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 (illustrator). …

Condition: Good. Good condition. No Dust Jacket A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains.

Paperback. Condition: New. Barnett, Alex (illustrator).

Condition: As New. Unread book in perfect condition. Barnett, Alex (illustrator).

Condition: New. Barnett, Alex (illustrator).

paperback. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

DVD. Condition: New. Still new in plastic.

paperback. Condition: Fine.

hardcover. Condition: Good. No dust jacket. Very minor wear.No marks in text.

Condition: New. Barnett, Alex (illustrator).

hardcover. Condition: Very Good.

Condition: New. 2024. Paperback. . . . . . Barnett, Alex (illustrator).

Condition: very_good. This book is in Very good condition. There may be a few flaws like shelf wear and some light wear.

Condition: New. Barnett, Alex (illustrator).

Condition: New. 2024. Paperback. . . . . . Books ship from the US and Ireland. Barnett, Alex (illustrator).

hardcover. Condition: Very Good. With wrap-around flap very good.

Hardcover. Condition: Very Good. No Jacket. Obi is present. Slight bump to head of spine cover.

Hardcover. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.