Y2K, what Every Consumer Should Know to Prepare for the Year 2000 Problem PDF Download
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Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
ISBN:
Category : Reference
Languages : en
Pages : 148
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Book Description
Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
ISBN:
Category : Reference
Languages : en
Pages : 148
Get Book Here
Book Description
Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
ISBN:
Category : Consumer education
Languages : en
Pages : 129
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Book Description
Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
ISBN:
Category : Consumer education
Languages : en
Pages : 129
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Book Description
Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
ISBN:
Category : Reference
Languages : en
Pages : 136
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Book Description
Author: Janet Abbate
Publisher: JHU Press
ISBN: 1421444372
Category : Computers
Languages : en
Pages : 473
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Book Description
"This anthology of original historical essays examines how social relations are enacted in and through computing using the twin frameworks of abstraction and embodiment. The book highlights a wide range of understudied contexts and experiences, such as computing and disability, working mothers as technical innovators, race and community formation, and gaming behind the Iron Curtain"--
Author: United States. Congress. House. Committee on Government Reform and Oversight
Publisher:
ISBN:
Category :
Languages : en
Pages : 540
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Book Description
Author:
Publisher:
ISBN:
Category : Finance, Personal
Languages : en
Pages : 258
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Book Description
Author: Wendy Hui Kyong Chun
Publisher: MIT Press
ISBN: 0262046229
Category : Technology & Engineering
Languages : en
Pages : 341
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Book Description
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.
Author: United States. Congress. House. Committee on Government Reform and Oversight. Subcommittee on the District of Columbia
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 74
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Book Description
Author: United States. Congress. Senate. Special Committee on the Year 2000 Technology Problem
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 56
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Book Description
