Y2K, what Every Consumer Should Know to Prepare for the Year 2000 Problem
Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
Published: 1998
Total Pages: 148
ISBN-13:
DOWNLOAD EBOOK →Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
Published: 1998
Total Pages: 148
ISBN-13:
DOWNLOAD EBOOK →Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
Published: 1999
Total Pages: 129
ISBN-13:
DOWNLOAD EBOOK →Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
Published: 1999
Total Pages: 129
ISBN-13:
DOWNLOAD EBOOK →Author: United States. Congress. House. Committee on Science. Subcommittee on Technology
Publisher:
Published: 1999
Total Pages: 136
ISBN-13:
DOWNLOAD EBOOK →Author: Janet Abbate
Publisher: JHU Press
Published: 2022-08-30
Total Pages: 473
ISBN-13: 1421444372
DOWNLOAD EBOOK →"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:
Published: 1997
Total Pages: 540
ISBN-13:
DOWNLOAD EBOOK →Author: Wendy Hui Kyong Chun
Publisher: MIT Press
Published: 2021-11-02
Total Pages: 341
ISBN-13: 0262046229
DOWNLOAD EBOOK →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:
Published: 1999
Total Pages: 74
ISBN-13:
DOWNLOAD EBOOK →Author: United States. Congress. Senate. Special Committee on the Year 2000 Technology Problem
Publisher:
Published: 1999
Total Pages: 56
ISBN-13:
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