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Critical data science is an interdisciplinary approach to data science that scrutinizes the social, ethical, and political dimensions of data collection, analysis, and interpretation. Unlike traditional data science, which often focuses on technical accuracy and predictive capability, critical data science questions how data practices may reinforce inequality, affect privacy, shape public discourse, and contribute to biased outcomes. Drawing on fields like sociology, ethics, and critical theory, it seeks to expose the implicit assumptions and power dynamics embedded in data-driven technologies, emphasizing transparency, accountability, and fairness to support just and equitable outcomes in data-driven decision-making. "We have a special guest today: Stefania Milan is a Professor of Critical Data Studies at the Department of Media Studies, University of Amsterdam. She was also Faculty Associate (2020-2022) at the Berkman Klein Center for Internet & Society of Harvard University. Her work explores the interplay between digital technology and data, political participation, and governance, focusing on infrastructure and agency. Stefania leads the project “Citizenship and standard-setting in digital networks” (in-sight.it), funded by the Dutch Research Council. Stefania holds a PhD in Political and Social Science from the European University Institute (2009). Before joining the University of Amsterdam, she worked at, among others, the Citizen Lab (University of Toronto), Tilburg University, and the Central European University."