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.
