Data Feminism

By: Catherine D'Ignazio, Lauren F. Klein

Narrated by: Teri Schnaubelt

Length: 7 hrs and 37 mins

Publisher's summary

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.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2020 Massachusetts Institute of Technology (P)2020 Tantor

a long pamphlet, zero value

it starts with a "check your privileges" list, and goes downhill from there. it's a book-sized manifesto with a lot of emotion and pathos, very little in terms of actionable information, or even a way to convince anyone who is not already bought in on the idea. it promotes a very strong group think mindset under the guise of pluralism.
"feminism" in this book seems to be defined as "everything that's good and inclusive", increasing the scope of this already out of focus book to even larger mess.

Brilliant! amust-read for all data scholars

Loved it, brilliant & infinitely illuminating, riveting, could hardly put it down - thank you for writing and recording this book!

Brilliant

A must read for anyone with an interest in data science or who uses big data in their work.