«Summary
A new way of thinking
about data science and data ethics that is informed by the ideas of
intersectional feminism.
The open access edition
of this book was made possible by generous funding from the MIT Libraries.
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». Saiba mais.
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