Cutting edge strategies for thinking about data science and data ethics through an
intersectional feminist lens. “Without ever finger-wagging Data Feminism reveals inequities
and offers a way out of a broken system in which the numbers are allowed to lie.”— WIRED
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.
