Catherine D’Ignazio and Lauren F. Klein, Data Feminism. The MIT Press, 2020. ISBN: 9780262358521 ISBN: 0262358522
Chapter Reviewed: Chapter 2
Reviewed by: Hannah Mendro, hsmendro@uw.edu
Chapter 2 of Data Feminism, “Collect, Analyze, Imagine, Teach,” focuses its attention on the creation and development of projects which gather and work with data. In a world where data counts for so much, and where powerful systems base their decisions so heavily on studies and statistical information, D’Ignazio and Klein draw our attention to the underlying structures that determine what data is collected and by whom. Continuing their ongoing exploration of the role of power and domination in data collection and teaching, they move from theories of power to the practice of data collection—and to the steps that can be taken to challenge and reimagine the structures of power that underlie it. The authors effectively illustrate not only the impossibility of neutrality in data collection and presentation, but also how principles of Black feminism and critical theory can be incorporated into the practice of gathering, sharing, and teaching data. Through the use of engaging and specific examples—and their constant returning to the framework of power that structures data collection—they make the concept of data feminism accessible and interesting even to readers who may perceive themselves as outside disciplines that rely on more traditional representations of data such as graphs, charts, and statistics.
Where the first chapter provided the theoretical foundations, an understanding of “how power operates in the world,” this chapter narrows in more specifically on the way power operates in data collection and visualization projects. If data is used to make information visible, who is collecting it and what are they drawing attention to? The chapter opens with a compelling example: the map “Where Commuters Run Over Black Children on the Pointes-Downtown Track” released by the Detroit Geographic Expedition and Institute (DGEI) (50). D’Ignazio and Klein point to this map as an example of data collected by, for, and with a community with a keen awareness of its own problems and the structural disadvantages that perpetuate them. The neighborhood that contributed to the map had been speaking up for quite some time about their children being run over by commuters, but until numbers and statistics were presented, city planners and legislators did not pay attention. These numbers are not the only kind of information that exists to illustrate a problem, but people in power typically demand this kind of collection and presentation in order to believe that a problem exists, let alone attempt to make any change. The fact that this data needs to be collected and presented effectively illustrates the disparity in the kind of data that is collected, who collects it, and what problems it purports to solve. The authors compare the DGEI map to past examples of official maps that reinforced power, such as redlining maps that codified discrimination in city planning. Placed side by side, these maps ground and illustrate the point that the collection and presentation of data are not neutral practices: data can reinforce or speak against power.
The map example creates an entry point into D’Ignazio’s and Klein’s underlying principle for this chapter: “Data feminism commits to challenging unequal power structures and working toward justice.” The chapter as a whole engages many other examples of data collection projects, such as Marie Saguero’s work “logging femicides in Mexico” (56) and Laurie Rubel’s Local Lotto project interrogating the impacts of the lottery on low-income families (67), which work according to this same principle. However, the introductory example of the DGEI map becomes a touchstone that the chapter can return to as a way of illustrating its point: that the very collection of data is also subject to interlocking structures of power and oppression, and that a feminist approach to data collection must always challenge those structures. The authors point out that who collects the data, what data is collected, and how it is used must all be asked intentionally throughout the process of collecting data, and must be answered in a way that challenges power structures. In thinking this through, they also analyze the very idea of data as proof. Data collection can be used in the name of challenging power—to point out the problems that marginalized communities are facing, for instance—but that proof is always presented to the oppressor; the communities facing the problems are already aware of the problems. So in this way, the use of data also falls into the trap of power. The goal of data feminism is to use data as a tool to challenge those power structures, with an awareness of the existing unfairness in the system they are trying to use.
As they think through these questions of how data collection can be used for anti-oppression purposes, Klein and D’Ignazio structure the chapter according to the four parts of its title: collect, analyze, imagine, teach, segueing further and further into more liberatory approaches to data work as they go. “Collect” and “analyze” are addressed in thinking about those questions of what, how, and by whom data is being collected. For “imagine” and “teach,” they move into more transformative methods of thinking. They encourage data collectors to think not only about how their work is interacting with structures of power, but to try to imagine outside those structures to think of new and different ways they can be doing this work, and how they can pass on new data traditions to their own students. They encourage data feminists to think about their projects from a perspective of “co-liberation,” working with communities not as an act of charity but from the perspective that liberation will free everyone. This is their form of radical imagination: thinking differently about the world and daring to dream that it can look different.
The structure of the chapter works well with the examples Klein and D’Ignazio use to illustrate how these approaches can change data collection for the better, and why this change matters. For a reader with little experience with data, the examples ground the arguments without veering into disciplinary jargon, and the argument builds on itself throughout the course of the chapter. Klein and D’Ignazio also do an excellent job interrogating both the projects they highlight and their own assumptions. At the end of the book, they provide an “audit” of their citations, demonstrating that their work often falls short of their goal of using examples from people of color and trans people. However, they interrogate the examples they are using, pointing out where these projects fail to live up to their own principles. This careful critique ensures that each example is intentional, chosen for quality rather than fitting a self-assigned quota, and is challenged when necessary.
As a reader with a cultural studies background, I am familiar—at least in concept—with the theory that Klein and D’Ignazio invoke in this book. I have spent time studying and engaging with questions of power and how it is wielded. The chapter provides plenty of footholds for my theory-driven experience, particularly in their exhortation to interrupt the workings of patriarchal power in the way that data is taught. But my work in humanities fields has left me with little experience personally gathering and analyzing the kind of numbers and statistics that the word “data” evokes. The examples throughout the chapter—and particularly the touchstone example of the DGEI map—help make the chapter accessible even to a reader like myself unfamiliar with the details of STEM fields and the quantitative measurements they require. Accessible, however, does not mean unchallenging. Regardless of how much experience its readers may have with data, or what fields we might be coming from, this chapter challenges us, just as the authors challenge themselves, to do better: to change our approaches, to examine our assumptions, and to imagine new ways of working with and teaching data.