Movement Data Science: Practices for Working in Solidarity with Social Justice Efforts

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Although many data scientists are eager to promote social justice, their efforts often fall short, and in some cases even exacerbate existing injustices. This gap between intentions and impacts follows from issues in how data scientists conceptualize their role in social justice efforts: they typically act as technicians, aiming to develop technical tools that will produce social justice. This default role generally results in depoliticized techno-fixes that fail to produce substantive changes. In this paper, we contribute “movement data science” as a framework to guide data scientists in taking on more productive and equitable roles in social justice efforts. Our framework is inspired by movement lawyering, a practice used by lawyers to situate themselves in grassroots social movements. Movement data science suggests that data scientists should ground their efforts to advance justice in sustained and solidaristic relationships with social movements. We contribute a process with four essential practices to help data scientists build these relationships and identify roles to play within specific social justice efforts. Movement data science does not provide a simple blueprint for data scientists to follow, but it can help them support social justice movements in substantive ways that go beyond attempting to solve injustices with technical tools.