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

Format:

Book

Author/Creator:

D'Ignazio, Catherine, author.

Klein, Lauren F., author.

Series:

ideas series.

<strong> Ideas Series

Language:

English

Subjects (All):

Feminism.

Cyberfeminism.

Feminism and science.

Big data--Social aspects.

Big data.

Quantitative research--Methodology--Social aspects.

Quantitative research.

Power (Social sciences).

feminism.

Medical Subjects:

Feminism.

Physical Description:

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

Place of Publication:

Cambridge, Massachusetts : The MIT Press, [2020]

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.

"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"-- Provided by publisher.

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, 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.

Notes:

Includes bibliographical references (pages [235]-301) and indexes.

ISBN:

9780262044004

0262044005

OCLC:

1119470058