Data-Pop Alliance Expands Its Data Feminism Program with Acquisition of Data Feminism Network

* Both DFN and DPA’s Data Feminism Program are inspired by, but unaffiliated with, the book Data Feminism, written by Catherina D’Ignazio and Lauren F. Klien, and published by the MIT Press.

A New Chapter for DPA's Data Feminism Program

At Data Pop Alliance (DPA), we are thrilled to announce our acquisition of Data Feminism Network (DFN), a significant step in our mission to “change the world with data”. DFN, a collaborative community and advocacy group dedicated to promoting feminist principles in data science and technology, will now have its leadership integrated into a newly-established advisory board for our Data Feminism Program.

In their new advisory roles, DFN’s Ali Dunn, Simran Panatch, Jade Greer, Elettra Baldi, and Mycala Gill will help refine the program’s strategy and solidify DPA as a welcoming partner for data professionals, social justice advocates, and newcomers alike to deepen their knowledge and expand their networks in the realm of data feminism. 

Data Feminism Network

Dunn founded DFN in 2020 while completing her Masters of Business Analytics at the University of British Columbia. What began as a modest side project soon blossomed into something far beyond her initial expectations. She established a strong team with five members across the U.S., Canada, and Mexico that helped cultivate a vibrant community with followers in over forty countries. The organization has since emerged as a passionate advocate for equitable and gender-sensitive data systems.

Inspired by Catherine D’Ignazio and Lauren Klein’s book, Data Feminism, the organization applies an intersectional feminist lens to their community-based initiatives focused on building awareness, exchanging knowledge, and collaborating with experts. These initiatives include book clubs, a podcast, and events, all designed to make information accessible to individuals, irrespective of their data literacy levels. Through these endeavors, the network works to equip decision makers across industries with the tools to effectively identify data biases that disproportionately benefit some and harm others.

A Synergetic Merger

DPA’s Data Feminism Program is also rooted in D’Ignazio and Klein’s foundational principles, as well as the 2030 Agenda for Sustainable Development’s Goal 5, which highlights the need to “achieve gender equality and empower all women and girls.” The program focuses on improving the availability, quality, and use of gender data through a combination of evidence-based gender assessments and data training, as well as through the development of DPA’s own unique initiatives. The aim of the program is to produce evidence for advocacy endeavors and policies that will accelerate intersectional, feminist, and LGBTQI+ inclusive gender equality.

What to Expect

So, what can you expect from this acquisition? As part of our commitment to inclusive data practices, we’ll be expanding DFN’s initiatives to new languages, making the community even more accessible and diverse. Additionally, look forward to a lineup of collaborative events and empowering discussions, spotlighting the combined leadership of both DPA and DFN. This November, join us at the Festival de Datos, where you’ll find sessions hosted by team members from DFN and DPA. To stay in the know about these thrilling developments and partnership events, be sure to connect with us on social media.

Share
Keywords
Author(s)
Share
Recommendations

Project Report

Stratégie Nationale des Données du Sénégal – Résumé

En 2023, DPA a fourni un soutien technique à l’élaboration de la

Project Report

Community-Based Social Protection Mechanisms in Africa’s Borderlands – Liberia and Sierra Leone Case Study

The report on “Community-based social protection mechanisms in Africa’s borderlands – Liberia

Book Chapter

Book Chapter “AI for SDGs—and Beyond? Towards a Human AI Culture for Development and Democracy”

Artificial intelligence (AI) can contribute to the United Nations Sustainable Development Goals