Making Data Justice a Priority

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​I recently read the book, Data Feminism by Catherine D’Ignazio and Lauren Klein.  It offers an insightful look at the many ways that data science mirrors and replicates social hierarchies and injustices.  The book starts with the story of Christine Darden, one of the women known as the “human computers” and her story of fighting against a racist, sexist workplace at NASA’s Langley Research center in the 1970s.  Throughout the book, it is important to the authors to ground data science in lived experience.  D’Ignazio and Klein offers this definition of data feminism:

“A way of thinking about data, both their uses and their limits, that is informed by direct experience, a commitment to action, and by intersectional feminist thought. The starting point for data feminism is something that goes mostly unacknowledged in data science:  power is not equally distributed in the world.  Those who wield power are disproportionately elite, straight, white, able-bodied, cisgender men from the Global North. The work of data feminism is first to tune into how standard practices of data science serve to reinforce those existing inequalities and second to use data science to challenge and change the distribution of power.  Underlying data feminism is a belief in co-liberation:  The idea that oppressive systems of power harm all of us, that they undermine the quality and validity of our work, and that they hinder us from creating true and lasting social impact with data science.”(p. 8)

D'Agnazio and Klein do a wonderful job analyzing power (who has it and who doesn’t) in data science with thoughtful, varied examples that start with stories of real lives then move into an analysis of all the ways data is used for or against groups of people. They also highlight inspiring stories of data activists who are reclaiming how data is used, collected, represented and contextualized to give power back to marginalized groups.  One particular story that stood out to me was the work of Maria Salguero who has individually documented all of the instances of femicides in Mexico over the past 5 years and provides personal data along with links to news reports about each victim.  Prior to her diligent work, the Mexican government did not have a database of femicides making it easy to blame the victims instead of understanding that it reflected a larger societal problem.  Another powerful story is a project of technology researcher Kate Crawford and design scholar Vladian Joler called the Anatomy of an AI System (https://anatomyof.ai/).  This project involves the creation of a data map representing the complicated production, usage, and recycling process of the Amazon Echo Dot.  Along with the map, there is a 9,000 word essay explaining the data map.  Not only does the map make visible the elusive workings of an AI system, it includes a contextualized explanation to help viewers really understand its process.  It is a powerful way to tell the story of data in AI.
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​Each chapter focuses on one of the seven principles of data feminism: examine power, challenge power, elevate emotion and embodiment, rethink binaries and hierarchies, embrace pluralism, consider context and make labor visible.  The book provides an important framework and analysis of bias and injustice in data science.  There is much work to be done and it is my hope that more people will begin thinking about data and demand more transparency and equity in the field. The book is available online from MIT Press for free: /data-feminism.mitpress.mit.edu/