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Data Feminism (Strong Ideas) de Catherine…
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Data Feminism (Strong Ideas) (edició 2020)

de Catherine D`Ignazio (Autor), Lauren F. Klein (Autor)

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1592170,573 (4.2)1
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 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.… (més)
Membre:MarcDam
Títol:Data Feminism (Strong Ideas)
Autors:Catherine D`Ignazio (Autor)
Altres autors:Lauren F. Klein (Autor)
Informació:MIT Press (2020), 328 pages
Col·leccions:Llista de desitjos
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Data Feminism (Strong Ideas) de Catherine D'Ignazio

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This is a fantastic introductory text for several fields— most notably intersectional feminism, data ethics/justice, and visual cultures. D'Ignazio and Klein dive into the highly relevant dangers that unmonitored data can cause, and use concrete and timely examples to ground their theories in reality. Instead of pointing out a problem and despairing, they delineate actionable steps relevant to anyone using digital technologies, even for the average "layperson" who just use sites such as Facebook or Twitter. This is a crucial text to anyone interested in what goes into data creation, maintenance, and/or use. This is a crucial text for anyone curious about how data manipulation impacts your day to day life. This is a crucial text for anyone interested in data activism.

D'Ignazio and Klein are in the privileged position to permit the free use of their book, and I recommend taking advantage of that! Their book is free to read online. I would especially recommend reading their Intro, Chapter 3, and Chapter 6 at the bare minimum. ( )
1 vota Nikki_Sojkowski | Aug 26, 2021 |
A beautifully designed and very clearly and excellently written manifesto on data presentation. Highly recommended for all. ( )
1 vota JBD1 | Jun 23, 2021 |
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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 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.

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