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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