Data Science as Political Action: Grounding Data Science in a Politics of Justice

Abstract

In response to increasing public scrutiny and awareness of the social harms associated with data-driven algorithms, the field of data science has rushed to adopt ethics training and principles. Such efforts, however, are ill-equipped to address broad matters of social justice. Instead of a narrow vision of ethics grounded in vague principles and professional codes of conduct, I argue, the field must embrace politics (by which I mean not simply debates about specific political parties and candidates but more broadly the collective social processes that influence rights, status, and resources across society). Data scientists must recognize themselves as political actors engaged in normative constructions of society and, as befits political practice, evaluate their work according to its downstream impacts on people’s lives. I justify this notion in two parts: first, by articulating why data scientists must recognize themselves as political actors, and second, by describing how the field can evolve toward a deliberative and rigorous grounding in a politics of social justice. Part 1 responds to three arguments that are commonly invoked by data scientists when they are challenged to take political positions regarding their work: “I’m just an engineer,” “Our job isn’t to take political stances,” and “We should not let the perfect be the enemy of the good.” In confronting these arguments, I articulate how attempting to remain apolitical is itself a political stance — a fundamentally conservative one (in the sense of maintaining the status quo rather than in relation to any specific political party or movement) — and why the field’s current attempts to promote “social good” dangerously rely on vague and unarticulated political assumptions. Part 2 proposes a framework for what a politically engaged data science could look like and how to achieve it. Recognizing the challenge of reforming the field of data science in this manner, I conceptualize the process of incorporating politics into data science as following a sequence of four stages: becoming interested in directly addressing social issues, recognizing the politics underlying these issues, redirecting existing methods toward new applications that challenge oppression, and developing practices and methods for working with communities as productive partners in movements for social justice. The path ahead does not require data scientists to abandon their technical expertise, but does require them to expand their notions of what problems to work on and how to engage with social issues.