Data feminism (Book, 2020) [WorldCat.org]
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Data feminism

Author: Catherine D'Ignazio; Lauren F Klein
Publisher: Cambridge, Massachusetts : The MIT Press, [2020]
Series: ideas series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense  Read more...
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Details

Document Type: Book
All Authors / Contributors: Catherine D'Ignazio; Lauren F Klein
ISBN: 9780262044004 0262044005
OCLC Number: 1119470058
Description: xii, 314 pages : illustrations (some color) ; 24 cm.
Contents: 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, ninja, 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.
Series Title: ideas series.
Responsibility: Catherine D'Ignazio and Lauren F. Klein.

Abstract:

"We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought"--

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