So, you want to be a feminist data scientist…here’s what you need to know

May 13, 2020

In April, Data2X hosted a book talk with Catherine D’Ignazio and Lauren F. Klein, the authors of Data Feminism. We were unable to answer the below questions from the audience during the book talk, so we turned them into a blog. Their answers have been edited for clarity and length. This blog is one of a two-part series; read the second blog here.

How do you maintain (relative) objectivity as a researcher with your own personal experience of power inequality?

The first step is to recognize that there is no such thing as pure objectivity. Everyone’s perspective offers valuable knowledge, but that perspective is influenced by a person’s intersectional identities — their gender expression, race, sexual orientation, socio-economic background, and more. Instead, what we should aim for is what feminist philosopher Donna Haraway has called, “feminist objectivity”, which gives more attention to the particular place and position from which you produce science and analysis. The goal is to generate more awareness — not less — of the experiences, privileges, and assumptions that people bring to the table and to join with others doing the same. Instead of thinking about your own experiences and identities as biasing your work, think about them as important perspectives that can frame and contribute to higher-quality work.

How might we include #datafeminism practices and principles as part of civic engagement for young people and teachers? How might we harness the power of young people’s attention right now to practice being civic data scientists?

In our book [Data Feminism], we talk about how to teach data as an intersectional feminist. In our opinion, the way it’s currently taught values individual technical mastery over collaboration and social impact. As a result, women and people of color are often left behind in data science.

What’s the alternative? We believe data science should connect abstract concepts with real-life scenarios to challenge power and create social impact in the communities around us. A great example of this is a project called Local Lotto, which engages high school students in New York City to look at the impact of the lottery on their neighborhood using concepts related to data and statistics. In New York, low-wage workers buy more lottery tickets than those in a higher socioeconomic bracket, but money from lottery ticket sales aren’t allocated back to those workers or their communities.

By combining these equity considerations with data science, Local Lotto teaches students how to collect, analyze, and use data in a way that centers social issues in their own communities. They asked students to do work with census data, maps, and interviews with people in the community to come up with an answer to the question: “Is the lottery good or bad for your neighborhood?” Applied teaching, or introducing concepts in a meaningful social impact context, can help establish connections between young people and their communities and between data science and social impact.

Do you think initiatives to build feminist data can start as an individual initiative (instead of by organizations)? If yes, should I start by sector or by territory?

Working individually or with an organization is not mutually exclusive, and there are multiple ways to go about building feminist data. If you want to start something, we always recommend doing a prior scan of work to make sure that what you are doing is actually new, because sometimes it might not need to take form as a new initiative or organization if someone is already doing something similar. Try to change what you can from where you are. If you’re intentional with what you set out to do, then the sum of all individual and organizational efforts to build feminist data are powerful. In the book, we feature María Salguero, an individual who created a comprehensive dataset on femicide in Mexico. While she’s not part of an official group or organization, her work has been immensely powerful in gaining attention and action from the Mexican government. At the same time, working with an institution can provide staff and financial resources that are key for strengthening feminist data initiatives.

Do you feel that people should formally learn data science/analytics in order to teach and do research on related subjects properly?

Not necessarily. We advocate for an expanded definition of data science that includes not only people with technical backgrounds but also people well-versed in data journalism, data communications, data art/visualization, and more. While technical expertise is important, there are many additional kinds of credentials and expertise that are valuable because they bring different perspectives to data science.

That said, it’s important to be responsible when working with data, so we recommend having basic background knowledge. You don’t necessarily need a fancy degree, but platforms such as YouTube, Coursera, and more can help you gain valuable knowledge to inform projects you’re working on. Many people in dominant groups don’t necessarily know more, they are just more confident and privileged in their learning.

Do you have any recommendations for books that have informed your thinking about these issues?

  • Race After Technology by Ruha Benjamin
  • Design Justice: Community-Led Practices to Build the Worlds We Need by Sasha Constanza-Chock
  • Algorithms of Oppression by Safiya Noble
  • Living a Feminist Life by Sara Ahmed
  • Automated Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks
  • Meaningful Inefficiencies: Civic Design in an Age of Digital Expediency by Eric Gordon and Gabriel Mugar

Watch the book talk.

Catherine D’Ignazio is an Assistant Professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT.

Lauren F. Klein is an Associate Professor of English and Quantitative Theory and Methods at Emory University.

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