233044 SE Data Welfare State (2026S)

Contents, aims and methods of course

While datafication, automation, and algorithmic prediction have become standard practices by big tech companies and their data-driven business models, they are increasingly introduced in public institutions and urban planning nowadays. Recent scandals such as the childcare benefits scandal in the Netherlands or the Robodebt scandal in Australia have shown how algorithmic systems can lead to bias, discrimination, and human rights violations in welfare states. Contrary, high hopes are attached to open government, citizen science, and participatory urban planning; especially in times of accelerated climate change. This seminar will address the hopes, fears, and ambivalences related to the data welfare state in the context of European policy, public institutions, and urban spaces around the world. The following questions will guide our discussions: How are datafication, automation, and Artificial Intelligence (AI) rhetorically shaped in EU policy? How are algorithmic systems introduced in organizational practices, what implications do they cause, and how could they be collectively managed? What role do data, apps, and, algorithmic predictions play in city planning? How could citizen science, participation, and collective knowledge production lead to more democratic ways of urban planning? What could we learn from the Global South, the “Majority World”, regarding algorithmic surveillance, but also data narratives from below?

To answer these questions, students will learn how to critically engage with literature at the intersection of STS, critical data studies, and citizen science and how to relate theoretical concepts to empirical case studies. Starting from students’ own experiences with datafication and automation, we will discuss European policy imaginaries, (semi-)automated decision-making in public administration, algorithmic bias, discrimination & “data care”, data-driven urban planning, participatory knowledge production, and data narratives in global cities. Each unit will be composed of discussions based on compulsory literature, and an empirical case study presented by student groups (based on suggested readings and a related empirical “case”). This study could serve as groundwork for the term paper, in which each student will individually outline, or conduct, a small empirical project by combining the seminar literature with a small-scale “study” - e.g. analysis of a particular “scandal” in policy, media, or online spaces, exploration of an app, algorithmic system or AI tool, a citizen science approach, interview(s) with users or case workers – related to public institutions or urban spaces. In addition, we will conduct a participatory data walk together with a citizen science expert and talk to a Brazilian research activist creating data narratives together with residents of the favelas of Rio de Janeiro. The main aim of the seminar is to collectively learn how to combine theoretical concepts with empirical research, how to give and get feed-back on students’ projects, and how to outline/conduct a small empirical study embedded in the seminar literature. Ongoing discussions of written assignments, hands-on exercises, and oral presentations will help students to write their term papers. Teaching and readings are in English and students must be able to write and present their assignments in English.