Main entry:

D'Ignazio, Catherine, author.

Title & Author:

Data feminism / Catherine D'Ignazio and Lauren F. Klein.

Publication:

Cambridge, Massachusetts : The MIT Press, [2020]
©2020

Description:

xii, 314 pages : illustrations (some color), maps (some color) ; 24 cm

Series:

<strong> Ideas Series

Notes:
Includes bibliographical references (pages [235]-301) and indexes.
Introduction : Why data science needs feminism -- The power chapter ; Principle: Examine power -- Collect, analyze, imagine, teach ; Principle: Challenge power-- On rational, scientific, objective viewpoints from mythical, imaginary, impossible standpoints ; Principle: Elevate emotion and embodiment -- "What gets counted counts" ; Principle: Rethink binaries and hierarchies -- Unicorns, janitors, ninjas, wizards, and rock stars ; Principle: Embrace pluralism -- The numbers don't speak for themselves ; Principle: Consider context-- Show your work ; Principle: Make labor visible -- Conclusion : now let's multiply; Our values and our metrics for holding ourselves accountable ; Auditing Data Feminism / Isabel Carter.

Summary:

"A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom Data science for whom Data science with whose interests in mind The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. 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 ccd3d3y&c@hallenge 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." -- Provided by publisher.

ISBN:

9780262044004 hardcover
0262044005 hardcover

Subject:

Feminism.
Cyberfeminism.
Feminism and science.
Big data Social aspects.
Quantitative research Methodology Social aspects.
Power (Social sciences)
Féminisme.
Cyberféminisme.
Féminisme et sciences.
Données volumineuses Aspect social.
Recherche quantitative Méthodologie Aspect social.
Pouvoir (Sciences sociales)
feminism.
Data Science
Feminismus
Women's movement.

Added entries:

Klein, Lauren F., author.
ideas series.

Holdings:

Location: Library main 315282
Call No.: 315282
Copy: 1
Status: Available