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By formal measures, the Local Lotto approach worked: before one school’s implementation of Local Lotto, only two of forty-seven learners were able to determine the correct number of possible combinations in a lottery example. Later, almost half (twenty-one of forty-seven) were successfully able to calculate the number of combinations. But perhaps more importantly, the 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. 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. Just as with the DGEI map and school, led by Gwendolyn Warren, the identities of the creators matter. Second, rather than modeling data science as abstract and technical, Local Lotto modeled 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, as the organizers noted in a 2016 paper for Cognition and Instruction. Many of these 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. 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.” Because race and ethnicity were not part of the teaching material, the teachers felt that they did not have the experience or background to discuss them explicitly and deflected those conversations. As they write in the paper, “Youth, and in this case youth of color, have different understandings about racial boundaries; theirs are differently nuanced and scaled than affluent, white, or adult perspectives.” 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. Most people, regardless of their wealth or level of education, know they are not going to win the lottery, after all. There is an element of imaginative fantasy in purchasing a ticket. The campaign slogan, “Hey, you never know ...” appeals as much to this fantasy as it does to the reality of the odds, and this fantasy has value too. In reflecting on the unintended sense of judgment experienced by the students, the course designers determined that, in the next iteration of the course, they would work 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 book—are constantly learning how to be better allies and accomplices across difference. In this process, what becomes most important is to “stay with the trouble,” as feminist philosopher Donna Haraway would say. Staying with the trouble means persisting in your work, especially when it becomes uncomfortable, unclear, or outright upsetting. One of the biggest strengths of the Local Lotto project is the courage of its creators to publicly, transparently, and reflexively interrogate themselves and their process, to detail their stumbling blocks, and to describe their commitments to doing better in the future.