Breath of Fresh Air: Diversity Heroes – Catherine D’Ignazio and Lauren F. Klein
As a community, we embrace our diversity; diversity makes us better, stronger. We cannot do enough to applaud all of our heroes in their diversity. They are people who are ACM members, volunteers or experts in their field. Starting from June 2020, we have been reaching out to a number of heroes about their tech career journey, about their perspective on intersectionality and reflect on initiatives for equality.
This month’s guests are Catherine D’Ignazio and Lauren F. Klein. This blog post was adapted from their book, Data Feminism (MIT Press, 2020). More information about the book can be found at datafeminism.io.
Catherine D’Ignazio is a scholar, artist/designer and hacker mama who focuses on feminist technology, data literacy and civic engagement. She has run reproductive justice hackathons, designed global news recommendation systems, created talking and tweeting water quality sculptures, and led walking data visualizations to envision the future of sea-level rise. With Rahul Bhargava, she built the platform Databasic.io, a suite of tools and activities to introduce newcomers to data science. Her research at the intersection of technology, design & social justice has been published in the Journal of Peer Production, the Journal of Community Informatics, and the proceedings of Human Factors in Computing Systems (ACM SIGCHI). Her art and design projects have won awards from the Tanne Foundation, Turbulence.org and the Knight Foundation and exhibited at the Venice Biennial and the ICA Boston. D’Ignazio is an Assistant Professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT. She is also Director of the Data + Feminism Lab which uses data and computational methods to work towards gender and racial equity, particularly in relation to space and place.
Lauren Klein is an associate professor in the departments of English and Quantitative Theory & Methods at Emory University, where she also directs the Digital Humanities Lab. Before moving to Emory, she taught in the School of Literature, Media, and Communication at Georgia Tech. Klein works at the intersection of digital humanities, data science, and early American literature, with a research focus on issues of gender and race. In 2017, she was named one of the “rising stars in digital humanities” by Inside Higher Ed. With Matthew K. Gold, she edits Debates in the Digital Humanities, a hybrid print-digital publication stream that explores debates in the field as they emerge. Her current project, Data by Design: An Interactive History of Data Visualization, 1786-1900, was recently funded by an NEH-Mellon Fellowship for Digital Publication.
How to Teach Data Science like an Intersectional Feminist
In 1971, the Detroit Geographic Expedition and Institute (DGEI) released a provocative map, Where Commuters Run Over Black Children on the Pointes-Downtown Track. The map (figure 1) uses sharp black dots to illustrate the places in the community where the children were killed. On one single street corner, there were six Black children killed by white drivers over the course of six months. On the map, the dots blot out that entire block.
The people who lived along the deadly route had long recognized the magnitude of the problem, as well as its profound impact on the lives of their friends and neighbors. But gathering data in support of this truth turned out to be a major challenge. So Gwendolyn Warren, a Detroit-based organizer, pursued an unlikely collaboration: an alliance between Black young adults from the surrounding neighborhoods and a group led by white male academic geographers from nearby universities (1). Through the collaboration, the youth learned cutting-edge mapping techniques and, guided by Warren, leveraged their local knowledge in order to produce a series of comprehensive reports, covering topics such as the social and economic inequities among neighborhood children and proposals for new, more racially equitable school district boundaries.
In the field of critical cartography, the DGEI reports are somewhat famous–and rightly so (2). The maps they contain make a powerful data-driven case for social change. In our book, Data Feminism, we make the case that the DGEI should be famous for an additional reason: Warren’s insistence that the academic geographers involved in the project give something back to the community whose knowledge they were drawing upon for their research. Warren recognized that while a single map or project could make a focused intervention, education would enable her community to come away with a longer-term strategy for challenging power. As it turned out, the institutional affiliations of the geographers enabled them to offer free, for-credit college courses, which they taught in the community for community members.
The “Man Factory” Model of Data Science Education
Warren recognized that access to education—and to data science education in particular—would have to be expanded in order for it to achieve its equalizing force. Unfortunately, however, Warren’s transformative vision has still yet to be felt in the traditional data science classroom. Women faculty comprise less than a third of computer science and statistics faculty. More than 80 percent of artificial intelligence professors are men (3). This gender imbalance, and the narrowness of vision that results, is compounded by the fact that data science is often framed as an abstract and technical pursuit. Steps like cleaning and wrangling data are presented as solely technical conundrums; there is less discussion of the social context, ethics, values, or politics of data (4). This perpetuates the myth that data science about astrophysics is the same as data science about criminal justice is the same as data science about carbon emissions. This limits the transformative work that can be done. Finally, because the goal of learning data science is modelled as individual mastery of technical concepts and skills, communities are not engaged and conversations are restricted. Instead, teachers impart technical knowledge via lectures, and students complete assignments and quizzes individually. We might call this model of teaching “the Horace Mann Factory Model of Data Science,” after Horace Mann, the nineteenth-century educational reformer and politician, who saw education as an equalizer of men, but only certain men (read: white, Anglo, Christian) and explicitly not women. So let’s just call it the Man Factory for short.
The Man Factory is really good at producing men, mainly elite white men like the ones who already lead the classes. It’s not as good at producing women data scientists, or nonbinary data scientists, or data scientists of color. For years, researchers and advocacy organizations have recognized that there are problems with this “pipeline” for technical fields; yet this research is framed around questions like “Why are there so few women computer scientists?” and “Why are women leaving computing?” (5) Note that these questions imply that it is the women who have the problem, inadvertently perpetuating a deficit narrative (6). Feminist scholars who are studying the issue are, not surprisingly, asking very different questions, like “How can the men running the Man Factory share their power?” and “How can we structurally transform STEM education together?” (7)
Feminist Data Science Education in Action
One person currently modelling an answer to these questions is Laurie Rubel, the math educator behind the Local Lotto project. If you were on the city streets of Brooklyn or the Bronx in the past five years, you may have inadvertently crossed paths with one of her data science classes. You probably didn’t realize it because the classes looked nothing like a traditional classroom (figure 2). Teenagers from the neighborhood wandered around in small groups. They were outfitted with tablets, pen and paper, cameras, and maps. They periodically took pictures on the street, walked into bodegas, chatted with passersby in Spanish or English, and entered information on their tablets.
Rubel is a leader in an area called mathematics for spatial justice, which aims to show how mathematical concepts can be taught in ways that relate to justice concerns arising from students’ everyday lives and to do so in dialogue with people in their neighborhoods and communities. The goal of Local Lotto was to develop a place-specific way of teaching concepts related to data and statistics grounded in considerations of equity (8). Specifically, Rubel and the other organizers of Local Lotto wanted young learners to come up with a data-driven answer to the question: “Is the lottery good or bad for your neighborhood?”
In New York, as in other US states that operate lotteries, lottery ticket sales go back into the state budget—sometimes, but not always, to fund educational programs (9). But lottery tickets are not purchased equally across all income brackets or all neighborhoods. Low-wage workers buy more tickets than their higher-earning counterparts. What’s more, the revenue from ticket purchases is not allocated back to those workers or the places they live. Because of this, scholars have argued that the lottery system is a form of regressive taxation—essentially a “poverty tax”—whereby low-income neighborhoods are “taxed” more because they play more, but do not receive a proportional share of the profit (10).
The Local Lotto curriculum was designed to expose high school learners to this instance of social inequality. They begin by talking about the lottery and the idea of probability by playing chance-based games. The learners then leave the classroom with the goal of collecting data about how other people experience the lottery, which takes them back into their neighborhoods. They map stores that sell lottery tickets. They record interviews with shopkeepers and ticket buyers on their tablets and then geolocate them on their maps. They take pictures of lottery advertising. Afterwards, the learners analyze their results and present them to the class. They examine choropleth maps of income levels, they make ratio tables, and they correlate state spending of lottery profits with median family income. (No surprise: there is no correlation.) Finally, they create a data-driven argument: an opinion piece supported with evidence from their statistical and spatial analyses, as well as their fieldwork (figure 3).
Local Lotto approach made math and statistics relevant to the students’ lives. One student shared that what he learned was “something new that could help me in my local environment, in my house actually,” and that after the course, he tried to convince his mother to spend less money on the lottery by “showing her my math book and all the work.” Spanish-speaking women in the class who didn’t often participate in classroom discussion became essential translators during the participatory mapping module. Several students went on to teach other teachers about the curriculum, both locally and nationally.
Learning from Local Lotto
What’s different about the Local Lotto approach to teaching data analysis and statistical concepts compared to the Man Factory? How is Local Lotto challenging power both inside and outside the classroom? First, it was woman-led: the project was conceived by three women leaders representing three institutions. Second, rather than modelling data science as abstract and technical, Local Lotto modelled a data science that was grounded in solving ethical questions around social inequality that had relevance for learners’ everyday lives: Is the lottery good or bad for your neighborhood? The project valued lived experience: the learners came in as “domain experts” in their neighborhoods. And it valued both qualitative data and quantitative data: the learners spoke with neighborhood residents and connected their beliefs, attitudes, and concerns to probability calculations. Learners used community members’ voices as evidence in their final projects. Third, rather than valorizing individual mastery of technical skills as the gold standard, learners worked together during every phase of the project. They used methods from art and design (like the creation of infographics and digital slideshows) to practice communicating with data.
Even as we celebrate these intentional pedagogical choices, the Local Lotto project still had its shortcomings, which stemmed from a basic fact: the teachers and course designers of the project were white and Asian, whereas the youth in the classes were predominantly Latinx and Black (11). This led to several issues. For instance, the curriculum designers had intended to focus primarily on income inequality, but they discovered that “the students consistently surfaced race.” However, the teachers felt that they did not have the experience or background to discuss race and ethnicity explicitly and deflected those conversations. The organizers are now taking steps to explicitly integrate discussions about race into the curriculum, as well as to include race, ethnicity, and age data in the course projects.
The course designers also encountered “limited but recurring instances of resistance from students” to the project’s central focus on income inequality. They attribute this resistance to the fact that the course was developed and taught by outsiders and could be seen as passing judgment on the people in their neighborhoods: that because they were not from the community, the teachers were perpetuating a deficit narrative about low-income people. This is both a sophisticated and very fair pushback from the young learners. In the next iteration of the course, the course designers plan to connect students with people in the communities themselves who are actively working to address issues of income inequality.
In both its successes and its failures, as well as its commitment to iteration and trying again, Local Lotto encapsulates what it means to challenge power and privilege and work toward justice. Justice is a journey. The discomfort that comes along with this journey is par for the course. There is no such thing as mastery of feminism because those who hold positions of privilege—like those in data science, like the Local Lotto course designers, and like us, the authors of this essay—are constantly learning how to be better allies and accomplices across differences. In this process, what becomes most important, as feminist philosopher Donna Haraway would say, is to “stay with the trouble,” (12) —to persist in your work, especially when it becomes uncomfortable, unclear, or outright upsetting.
Footnotes
(1) Gwendolyn Warren, “About the Work in Detroit,” in Field Notes No. 3: The Geography of Children, Part II (East Lansing, MI: Detroit Geographical Expedition and Institute, 1971), 12.
(2) See, for example, Gwendolyn C. Warren, Cindi Katz, and Nik Heynen, “Myths, Cults, Memories, and Revisions in Radical Geographic History: Revisiting the Detroit Geographic Expedition and Institute” in Spatial Histories of Radical Geography: North America and Beyond, ed. Trevor J. Barnes and Eric Sheppard. New York: Andrew Wiley, 2019.
(3) From S. M. Wes, M. Whittaker, and K. Crawford, Discriminating Systems: Gender, Race and Power in AI (April 2019, AI Now Institute), https://ainowinstitute.org/discriminatingsystems.html
(4) This does not mean there are no data ethics courses, only that it is not the norm to address these concerns in introductory coursework. There is a list compiled by social computing researcher Casey Fiesler of hundreds of courses that specifically address ethics in technical fields at http://bit.ly/tech-ethics-syllabi.
(5) An April 2019 report from the AI Now Institute, has an excellent characterization of pipeline research and its shortcomings. See Sarah Myers West, Meredith Whittaker, and Kate Crawford, Discriminating Systems: Gender, Race, and Power in AI, https://ainowinstitute.org/discriminating systems.pdf.
(6) A deficit narrative reduces a group or culture to its “problems,” rather than portraying it with the strengths, creativity, and agency that people from those cultures possess. For more on deficit narratives in data science, see Data Feminism 58-9.
(7) A deficit narrative reduces a group or culture to its “problems,” rather than portraying it with the strengths, creativity, and agency that people from those cultures possess. For more on deficit narratives in data science, see Data Feminism 58-9.
(8) These concepts ranged from basic topics like ratios and probability to more advanced ideas about combinatorics and modelling. For more on the City Digits curriculum, see “City Digits: Local Lotto,” Center for Urban Pedagogy, accessed July 30, 2019, http://welcometocup.org/Projects/CityStudies/CityDigits.
(9) For recent reportage on this phenomenon, see Meghan Keneally, “Mega Millions Lottery: Where Does Lottery Money Go in Different States,” ABC News, October 22, 2018, https://abcnews.go.com/US/mega-millions-lottery-lottery-money-states/story?id=58661412; and Peter O’Dowd, interview with Liberty Vittert, Here & Now, October 23, 2018, https://www.wbur.org/hereandnow/2018/10/23/where-do-lottery-profits-go.
(10) Laurie H. Rubel, Vivian Y. Lim, Maren Hall-Wieckert, and Mathew Sullivan, “Teaching Mathematics for Spatial Justice: An Investigation of the Lottery,” Cognition and Instruction 34, no. 1 (2016): 1–26.
(11) Rubel et al., “Teaching Mathematics for Spatial Justice.”
(12) Donna J. Haraway, Staying with the Trouble: Making Kin in the Chthulucene (Durham, NC: Duke University Press, 2016)