Data Feminism infographics by Catherine D’Ignazio, Lauren Klein and Marcia Diaz. 2020.

A new course named Data Feminism is introduced this coming fall. The course explores the intersection of data science and intersectional feminism and targets data scientists.

Data science is often considered a neutral science; thus, power and oppression may not be the topics that data scientists think about very often in their everyday work. However, challenging these assumptions, Amir H. Payberah, WASP-associated Professor and the Data Feminism course responsible, asserts otherwise:

“Data are never neutral; they are always the product of unequal social relations. They encoded the values and beliefs of the dominant groups who created them, resulting in models that can disproportionately harm marginalized communities. As data scientists, we usually strive to improve models’ performance and accuracy. Yet, this pursuit may come at the risk of introducing biases or causing harm to certain individuals or communities. While our efforts may enhance the well-being of 2% of the population, it’s crucial to acknowledge the possibility of inadvertently worsening conditions for others, and it is our responsibility to consider any potential harm.”

Intersectional feminism analysis

The course takes its analytical stance in intersectional feminism, which is a framework for understanding how different aspects of social and identity categories – such as race, class, gender, sexuality, and ability – intersect and overlap to shape individuals’ experiences of privilege and oppression. Acknowledging that data is power, that data is compiled and utilized unequally, and that different groups have different power positions in society, it is interesting to study the intersection of data science and intersectional feminism.

“There are similar courses in other places, mainly in the US – often called data justice, fairness in machine learning or similar, and lots of research has been done on the topic but not in these exact terms. There are lots of collisions and challenges we need to think about. Creating awareness is the first step. Solving the problem – that is an open and not so easy question,” says Amir H. Payberah.

The course is inspired by, but not limited to, the book “Data Feminism” by Catherine D’Ignazio and Lauren F. Klein. Prerequisites for participating students are completed courses in machine learning and deep learning and being familiar with Python. The goal of the course is to give the students knowledge to employ data and data science as tools to confront injustices and to be able to identify biases and take actions to address them.

Diving deeper into the literature

In addition to the course, Amir H. Payberah has started a book club on the same topic:

“Many people are interested to join. We are now around 40 people, ranging from students to professors. We will read one book per month and have our first session of discussion 28th of March.”

The course (PhD level) is run at KTH Royal Institute of Technology. It is not part of the WASP Graduate School course package, but it is open for WASP PhD students to participate. Registration for the course will open during spring. The book club is open for anyone who is interested in learning more about data feminism.

Read more about the course and the book club:


Amir H. Payberah

Associate Professor, Division of Software and Computer Systems, KTH Royal Institute of Technology

Published: March 22nd, 2024

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