Data Feminism

The featured image is cover art from "Data Feminism" by Catherine D'Ignazio and Lauren Klein.

Upcoming Events

How to participate: DataFeminism meetups occur monthly via Zoom on a variable schedule. Registration is required. Want to get involved? Contact us!

12-FebCritical approach to data visualizationEmily Merchant, Lindsay Poirier, Pamela Reynolds

About Data Feminism

Computers and code are part of sociotechnical systems that embody the biases of our society and data, and reflect existing structures of inequality. Increasingly, data science has the potential to reveal the sources of inequality and inform social justice projects. This working group explores the interaction between systems of power and oppression within the development and application of the data sciences. Specifically, this working group examines the intersection of feminist theory, critical data studies, and data science within four themes surrounding data feminism: feminist critiques of data science, causes and consequences of the lack of diversity in the data sciences, how mis-applications of data science perpetuate social inequality, and critical and participatory data science. By encompassing considerations of data collection, management, analysis, and storytelling, participants develop their own working definition of “data feminism” and a variety of frameworks and approaches to bring feminist theory into their own work, undergraduate education, the consciousness of data scientists, and ultimately the process of data-driven discovery.

This working group is led by Professors of Science and Technology Studies Emily Merchant and Lindsay Poirier, and DataLab Associate Director Pamela Reynolds, with founding support from the UC Davis DataLab and Feminist Research Institute.

Want to follow along or run a similar group for your community? Check out our reading list, and check back here for links to the products from this working group, including curriculum development materials.

The featured image is cover art from “Data Feminism” by Catherine D’Ignazio and Lauren Klein.