The Seven Principles of Data Feminism

Catherine d’Ignazio and Lauren Klein

In November 2015, Catherine, who was based out of the MIT Media Lab at the time, was invited by Mushon Zer-Aviv to write a blog post for the Responsible Data Forum – an event he was co-organising for January 2016. At the same time, Lauren, who was working at Georgia Tech in Atlanta, was preparing to travel to the NULab at Northeastern University, in Boston, to give a talk on some new research. Unbeknownst to each other, both of us had decided to focus on the same unusual topic: feminist data visualisation. Struck by the coincidence, a mutual friend put us in touch, and we soon began planning a collaboration. 

Our first work together was a short paper, Feminist Data Visualisation, which was in many ways inspired by the conversations and design workshops about ethics in visualisation that took place at the Responsible Data Forum. But as we continued to develop the concept, in conversation with each other and within our respective communities of practice, we realised that a feminist data visualisation, or any data visualisation, represents the output of a much longer and more complicated set of processes. 

We also realised that a feminist approach to data visualisation would need to consider the social, political, and historical context in which these processes took place. And so the concept of feminist data visualization evolved into data feminism: a way of thinking about data, data systems, and data science that is informed by the rich history of feminist activism and feminist critical thought. 

Data feminism begins with a belief in gender equality, and a recognition that achieving equality for people of all genders requires a commitment to examining the root cause of the inequalities that certain groups face today. 

Data feminism is not only about women. It takes more than one gender to have gender inequality and more than one gender to work toward justice. Similarly, data feminism isn’t only for women. Many men, nonbinary people and genderqueer people are proud to call themselves feminists and use feminism in their work. 

Furthermore, data feminism isn’t only about gender. Intersectional feminists like Kimberlé Crenshaw, bell hooks and the Combahee River Collective have taught us how race, class, sexuality, ability, age, religion, geography, and more are factors that work together to influence each person’s experiences and opportunities in the world. Intersectional feminism also teaches us that these experiences and opportunities (or the lack of opportunities, as the case may be) are the result of larger structural forces of power, which must be challenged and changed. In our contemporary world, data is power too. And because the power of data is wielded unjustly, it too must be challenged and changed. 

Underlying this commitment to challenging power is a belief in co-liberation: the idea that oppressive systems harm all of us, that they undermine the quality and validity of all of our work, and that they hinder all of us from creating true and lasting social impact. To guide us in this project, we have developed seven core principles. Individually and together, these principles emerge from a foundation in intersectional feminist thought. 

The seven principles of data feminism are as follows: 

  • Examine power. Data feminism begins by analysing how power operates in the world. 
  • Challenge power. Data feminism commits to challenging unequal power structures and working toward justice. 
  • Elevate emotion and embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world. 
  • Rethink binaries and hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression. 
  • Embrace pluralism. Data feminism insists that the most complete knowledge comes from synthesising multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing. 
  • Consider context. Data feminism asserts that data is not neutral or objective. It is the product of unequal social relations, and this context is essential for conducting accurate, ethical analysis. 
  • Make labour visible. The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labour visible so that it can be recognised and valued. 

In our book, Data Feminism (MIT Press, 2020), we explore each of these principles in more detail, drawing upon examples from the field of data science, expansively defined, to show how that principle can be put into action. 

Along the way, we introduce key feminist concepts like the matrix of domination (Patricia Hill Collins), situated knowledge (Donna Haraway), and emotional labour (Arlie Hochschild), as well as some of our own ideas about what data feminism looks like in theory and practice. To this end, we introduce readers to a range of folks at the cutting edge of data and justice. These include engineers and software developers, activists and community organisers, data journalists, artists, and scholars

This variety of people, and the variety of projects they have created or helped to create, is our way of answering the question: What makes a data science project feminist? As we assert, a data science project may be feminist in content, in that it challenges power by choice of subject matter; in form, in that it challenges power by shifting the aesthetic and/or sensory registers of data communication; and/or in process, in that it challenges power by building participatory, inclusive processes of knowledge production. What unites this broad scope of data work is a commitment to action and a desire to remake the world to be more equitable and inclusive. 

Our overarching goal is to take a stand against the status quo against a world that unfairly benefits rich white cisgender heterosexual non-disabled white men from the global north at the expense of others. 

Our principles are intended to function as concrete steps to action for data scientists seeking to learn how feminism can help them work toward justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. They are also addressed to professionals in all fields in which data-driven decisions are being made, as well as to communities that want to resist or mobilise the data that surrounds them. 

They are written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to communicate the significance of such charts and statistics to others. 

Borrowing from bell hooks, we say: data feminism is for everyone. Data feminism is for people of all genders. It’s by people of all genders. And most importantly: it’s about much more than gender. Data feminism is about power, about who has it and who doesn’t, and about how those differentials of power can be challenged and changed. 

More About Data Feminism

Data Feminism is an open access book published by MIT Press in 2020. You can read it for free online at or buy it from your local independent bookstore.