Data Feminism

Data Literacy, Data Science, Data Visualization, Feminism

A book that asks and answers the broad question, “What would an intersectional feminist data science look like?”

Co-authored with Lauren F. Klein

Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind?

The narratives around big data and data science are overwhelmingly white, masculine, and techno-heroic. In Data Feminism, we present away of thinking about data science and data ethics that is informed by intersectional feminist thought.

Process

For the publication of Data Feminism we worked closely with MIT Press to host an open and public peer review process on the PubPub platform where the draft garnered more than 14,000 views and 900 comments and suggestions. According to MIT Press, it is the most widely viewed and commented book published on PubPub to date.

Outcome

Data Feminism has been cited in scholarly works more than 1500 times in just over three years since it was published. I have given 30+ keynote talks and 80+ invited talks and panels about the book around the world.

The Civic Software Foundation, a non-profit organization in the U.S., used Data Feminism as the ethical framework for a national-scale metadata program. Pollicy, a technology consulting firm in East Africa, used the book as the basis for “Afrofeminist Data Futures,” a project about data practices that support feminist movements in sub-Saharan Africa. Data Feminism has been the subject of three stand alone conferences in Spain and Denmark.

The Data Feminism Network, a community organization in Canada, regularly hosts social and speaker events highlighting practitioners of data feminism in different sectors. Reading groups have been organized by UC Davis, Harvard University, Cornell University, University of Rochester, and the US Conference on Teaching Statistics, among others.

We were invited to participate in several international exhibitions and worked with designer Marcia Diaz to create infographics of the Data Feminism principles in ten languages: English, Spanish, Portguese, French, Korean, Italian, Greek, Turkish, Chinese, and Hebrew. The graphics can be downloaded for free from the book’s website: http://datafeminism.io.

Principles of Data Feminism

The book offers seven principles for analyzing existing power imbalances in data science and working toward justice.

  1. Examine Power: Data feminism begins by analyzing how power operates in the world.
  2. Challenge Power: Data feminism commits to challenging unequal power structures and working toward justice.
  3. 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.
  4. Rethink Binaries and Hierarchies: Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.
  5. Embrace Pluralism: Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing.
  6. Consider Context: Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis.
  7. Make Labor Visible: The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued.

(Left) Presentation screenshot and (right) sketch notes by audience member Elvia Vasconcelos from the last Data Feminism Reading Group meeting.

Reading Group

Between April-June 2020, Klein and I hosted nine virtual Data Feminism Reading Group meetings. Each week, we presented highlights from each book chapter, followed by a Q&A with the audience. Between 200-400 people joined us from around the world each week.

Student Researcher

Isabel Carter

Website

datafeminism.io

Translations

Spanish, 2023; Farsi, in process; Portuguese, in process

Publisher

The MIT Press, Cambridge, MA, Strong Ideas Series

Exhibitions

  • “Pattern Recognition – Through the Algorithmic Lens.” Stegi Onassis Foundation Athens, Athens, 2021.
  • “Design and Utopia: Immediate actions for a better present.” Abierto Mexicano de Diseño, Mexico City, 2020.

Awards for Data Feminism

  • Modern Language Association Prize for Collaborative, Bibliographical, or Archival Scholarship, 2022.
  • Association of American Publishers, Computing and Information Sciences, PROSE Award Finalist, 2021.
  • American Studies Association, Digital Humanities Caucus Book Award, 2021.
  • American College and Research Libraries, Outstanding Academic Title, 2020.

Selected Reviews

  • Computers & Culture Journal, April 2023.
  • IEEE Technology & Society Magazine, 2022.
  • RGWS: A Feminist Review, Oct. 2022.
  • Gender, Work and Organization Journal, August 2022.
  • American Association of Geographers, July 2022.
  • Digital Humanities Quarterly, June 2022.
  • Design & Culture Journal, March 2022.
  • The English Association, August 2021.
  • Social Movement Studies Journal, August 2021.
  • Theory, Research, and Action in Urban Education, Spring 2021.
  • Catalyst, April 2021.
  • American Scientist, Janurary 2021.
  • Venture Beat, December 2020.
  • LSE Review of Books, Oct. 2020.
  • Design Journal, October 2020.
  • Information, Communication & Society, October 2020.
  • The British Psychological Society, August 2020.
  • Times Higher Ed, April 2020.
  • WIRED, February 2020.

Selected Press

  • “El feminismo tiene mucho que ofrecer a la ciencia de datos.” Alternativas Económicas magazine, Mariana Vilnitzsky, June 14, 2021.
  • “Data Feminism: D’ignazio and Klein Call Out Inequality in Data.” IEEE Women in Engineering Magazine, Katianne Williams, May 6, 2021.
  • “L’algoritmo è sessista?” Corriere della Sera, Elena Papa, March 17, 2021.
  • “Power Structures, Data Feminism & Inequality.” Ocean Protocol, Diksha Dutta, August 18, 2020.
  • “Data Feminism: Intersectional Benchmarks for a Data-Driven World.” The Dialogue, Trisha Pande, August 2020.
  • “If You See a Binary, It Usually Hides a Hierarchy.” Deep Dives, Joana Varon, July 24, 2020.
  • “If AI Is Biased, How Should We Use It?” Harvard Tech Review, Salena Prakah-Asante, May 16, 2020.“Catherine D’Ignazio: ‘Data Is Never a Raw, Truthful Input – and It Is Never Neutral.’” The Guardian, Zoe Corbyn, March 21, 2020.
  • “The Elephant in the Server Room.” MIT News, Peter Dizikes, March 9, 2020.

Excerpted in

  • BBC Science Focus, February 2020.
  • Utne Reader, March 2020.
  • Ms. Magazine, April 2020.
  • Malofiej, June 2020.
  • The UX Collective, March 2021.
  • MIT Case Studies in Social and Ethical Responsibilities of Computing, Month 2021.

Interviews and Podcasts

  • WONDROS Podcast, June 2021.
  • Mindscape Podcast, June 2021.
  • The Goop Podcast, June 2021.
  • Women’s Protection and Empowerment (WPE) Podcast, February 2021.
  • Hala Bedi, January 2021.
  • Databite, May 2020.
  • We Be Imagining, April 2020.
  • New Books in Technology, March 2020.
  • The MIT Press Podcast, October 2019.
  • PolicyViz Podcast, December 2018.
  • Data Stories Podcast, November 2017.

Funders

Emerson College Mann Stearns Award, American Council of Learned Societies Collaborative Fellowship

Data Feminism cover image: Digital visualizations by Christopher Pietsch and Siqi Zhu from Art of the March, an archival project led by Alessandra Renzi, Dietmar Offenhuber, and Nathan Felde, based on posters collected from the 2017 Boston Women’s March.