Data Feminism
Catherine D'Ignazio · Lauren F. Klein
āļ.āļĒ. 2020 · Tantor Media Inc · āļāļĢāļĢāļĒāļēāļĒāđāļāļĒ Teri Schnaubelt
3.0star
2 āļĢāļĩāļ§āļīāļ§report
headphones
āļŦāļāļąāļāļŠāļ·āļāđāļŠāļĩāļĒāļ
7 āļāļĄ. 37 āļāļēāļāļĩ
āļāļāļąāļāļŠāļĄāļāļđāļĢāļāđ
family_home
āļĄāļĩāļŠāļīāļāļāļīāđ
info
reportāļāļ°āđāļāļāđāļĨāļ°āļĢāļĩāļ§āļīāļ§āđāļĄāđāđāļāđāļĢāļąāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļĒāļ·āļāļĒāļąāļ Â āļāļđāļāđāļāļĄāļđāļĨāđāļāļīāđāļĄāđāļāļīāļĄ
āļāļąāļāļāļąāļ§āļāļĒāđāļēāļāļĢāļ°āļĒāļ°āđāļ§āļĨāļē 46 āļāļēāļāļĩ āđāļāđāļāļļāļāđāļĄāļ·āđāļāđāļĄāđāļāļāļ°āļāļāļāđāļĨāļāđÂ
āđāļāļīāđāļĄ
āđāļāļĩāđāļĒāļ§āļāļąāļāļŦāļāļąāļāļŠāļ·āļāđāļŠāļĩāļĒāļāđāļĨāđāļĄāļāļĩāđ
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 ethicsâone 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.
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.
āļāļēāļĢāđāļŦāđāļāļ°āđāļāļāđāļĨāļ°āļĢāļĩāļ§āļīāļ§
3.0
2 āļĢāļĩāļ§āļīāļ§
āđāļŦāđāļāļ°āđāļāļāļŦāļāļąāļāļŠāļ·āļāđāļŠāļĩāļĒāļāļāļĩāđ
āđāļŠāļāļāļāļ§āļēāļĄāđāļŦāđāļāļāļāļāļāļļāļāđāļŦāđāđāļĢāļēāļĢāļąāļāļĢāļđāđ
āļāđāļāļĄāļđāļĨāļāļēāļĢāļāļąāļ
āļŠāļĄāļēāļĢāđāļāđāļāļāđāļĨāļ°āđāļāđāļāđāļĨāđāļ
āļāļīāļāļāļąāđāļāđāļāļ Google Play Books āļŠāļģāļŦāļĢāļąāļ Android āđāļĨāļ° iPad/iPhone āđāļāļāļāļ°āļāļīāļāļāđāđāļāļĒāļāļąāļāđāļāļĄāļąāļāļīāļāļąāļāļāļąāļāļāļĩāļāļāļāļāļļāļ āđāļĨāļ°āļāđāļ§āļĒāđāļŦāđāļāļļāļāļāđāļēāļāđāļāļāļāļāļāđāļĨāļāđāļŦāļĢāļ·āļāļāļāļāđāļĨāļāđāđāļāđāļāļļāļāļāļĩāđ
āđāļĨāđāļāļāđāļāļāđāļĨāļ°āļāļāļĄāļāļīāļ§āđāļāļāļĢāđ
āļāļļāļāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāļŦāļāļąāļāļŠāļ·āļāļāļĩāđāļāļ·āđāļāļāļēāļ Google Play āđāļāļĒāđāļāđāđāļ§āđāļāđāļāļĢāļēāļ§āđāđāļāļāļĢāđāļāļāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāļāļāļāļāļļāļ
