Data Feminism

D'Ignazio, Catherine; Klein, Lauren F.. Data Feminism. Cambridge, MA: MIT Press, 2020.

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Abstract (English)

"In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.

Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”

Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed." -- Publisher's website.

Types: Monographs
All Contributors: D'Ignazio, Catherine (Author); Klein, Lauren F. (Author); Carter, Isabel (Postfacer)
Dossier: 700 - FEMMES DANS LES ARTS - FÉMINISME / WOMEN IN THE ARTS - FEMINISM
Collation: xii, 314 pages : colour illustrations ; 24 cm
ISBN: 9780262044004
Language of Publication: English
Publishers: Cambridge, MA: MIT Press
Keywords: SCIENCE; ETHICS; CLASSIFICATION; INTERSECTIONALITY; OBJECTIVITY; POWER
Copyright Statement: Massachusetts Institute of Technology; Creative Commons CC BY-NC-ND
Notes:

Includes bibliographical references and index.

Series Name: <strong> Ideas Series / Edited by David Weinberger
Deposited by: Artexte Catalogueur
Date Deposited: 17 Jan 2023 21:45
Last Modified: 24 Feb 2024 21:09
URI: http://e-artexte.ca/id/eprint/34583
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