Critical Theory of Data Science

The application of Critical Theory to data science—examining how data is collected, analyzed, and used, and how these practices reflect and reinforce power relations. Critical Theory of Data Science asks: Whose data is collected? Who controls the algorithms? How do data systems encode bias and discrimination? Who benefits from data-driven decision-making, and who is harmed? Drawing on critical data studies, feminist technology studies, and surveillance studies, it insists that data is never raw—it's always cooked in contexts of power. Algorithms aren't neutral; they're politics in code. Understanding data science requires understanding who it serves.

"Data doesn't lie, they say. Critical Theory of Data Science asks: who collected it? For what purpose? With what biases? Algorithms trained on historical data reproduce historical injustices. Data science can liberate or control; it depends on who's doing it and why. Critical theory insists on asking: whose interests are served by this model, and whose are erased?"

Critical Theory of Data Science by Abzugal Nammugal Enkigal March 4, 2026