“Data” can seem like an abstract term – What counts as data? Who decides what is counted? How is data created? What is it used for?
These questions are some of the ones you might ask when applying a Data Feminist framework to you research. Data Feminism goes beyond looking at the mechanics and logistics of data collection and analysis to undercover the influences of structural power and erasure in the collection, analysis, and application of data.
Data Feminism was developed by Catherine D’Ignazio and Lauren Kline, authors of the book Data Feminism. Their ideas are grounded in the work of Kimberle Crenshaw, the legal scholar credited with developing the concept of intersectionality. Using this lens, they seek to undercover the ways data science has caused harm to marginalized communities and the ways data justice can be used to remedy those harms in partnership with the communities we aim to help.
The Seven Principles of Data Feminism include:
- Examine power
- Challenge power
- Rethink binaries and hierarchies
- Elevate emotion and embodiment
- Embrace pluralism
- Consider context
- Make labor visible
Applying data feminist principles to your research might involve working with local communities to co-create consent forms, using data collection to fill gaps in available data about marginalized groups, prioritizing the use of open source, community-created tools, and properly acknowledging and compensating people involved in all stages of the research process. At the heart of this work is the questioning of whose interests drive research and how we can reorient those interests around social justice, equity, and community.
The Feminist Data Manifest-No, authored in part by Anita Say Chan, Associate Professor in the School of Information Sciences and the College of Media, provides additional principles to commit to in data feminist research. These resources, and the scholars and communities engaged in this work, demonstrate how data and research can be used to advance justice, reject neutrality, and prioritize those who have historically experienced the greatest harm at the hands of researchers.
The Data + Feminism Lab at the Massachusetts Institute of Technology, directed by D’Ignazio, is a research organization that “uses data and computational methods to work towards gender and racial equity, particularly as they relate to space and place”. They are members of the Design Justice Network, which seeks to bring together people interested in research that centers marginalized people and aims to address the ways research and data are used to cause harm. These groups provide examples for how to engage in data feminist and data-justice inspired research and action.
Learning how to use tools like SPSS and NVivo is an important aspect of data-related research, but thinking about the seven principles of Data Feminism can inspire us to think critically about our work and engage more fully in our communities. For more information about data feminism, check out these resources: