We develop the concept of ‘aesthetic practices’ to capture the work needed for population data to be disseminated via government data portals. Specifically, we look at the Census Hub of the European Statistical System and the Danish Ministry of Education’s Data Warehouse. These portals form part of open government data initiatives, which we understand as governing technologies. We argue that to function as such, aesthetic practices are required so that data produced at dispersed sites can be brought into relation and (...) projected as populations in forms such as bar charts, heat maps and tables. Two examples of aesthetic practices are analysed based on ethnographic studies we have conducted on the production of data for the Hub and Warehouse: metadata and data cleaning. Metadata enables data to come into relation by containing and accounting for the differences between data. Data cleaning deals with the indeterminacies and absences of data and involves algorithms to determine what values data can obtain so they can be brought into relation. We attend to how both aesthetic practices involve normative decisions that make absent what exceeds them: embodied knowledge that cannot or has not been documented as well as data that cannot meet the forms required of data portals. While these aesthetic practices are necessary to sustain data portals as ‘sites of projection,’ we also bring critical attention to their performative effects for knowing, enacting and governing populations. (shrink)

Public sector adoption of AI techniques in welfare systems recasts historic national data as resource for machine learning. In this paper, we examine how the use of register data for development of predictive models produces new ‘afterlives’ for citizen data. First, we document a Danish research project’s practical efforts to develop an algorithmic decision-support model for social workers to classify children’s risk of maltreatment. Second, we outline the tensions emerging from project members’ negotiations about which datasets to include. Third, we (...) identify three types of afterlives for citizen data in machine learning projects: (1) data afterlives for training and testing the algorithm, acting as ‘ground truth’ for inferring futures, (2) data afterlives for validating the algorithmic model, acting as markers of robustness, and (3) data afterlives for improving the model’s fairness, valuated for reasons of data ethics. We conclude by discussing how, on one hand, these afterlives engender new ethical relations between state and citizens; and how they, on the other hand, also articulate an alternative view on the value of datasets, posing interesting contrasts between machine learning projects developed within the context of the Danish welfare state and mainstream corporate AI discourses of the bigger, the better. (shrink)

This paper explores “the peopling of Europe through data practices” in relation to standardized testing of students in Denmark. Programme for International Student Assessment is a central component of Danish and European education infrastructures. In Denmark, mediocre PISA results spurred the introduction of national testing. With inspiration from Michel Foucault’s notion of biopolitics, this paper analyzes how complementary Danish national test assessment scales make up population objects and student subjects and how these scales are aligned with European and transnational standards. (...) A norm scale, standardized against the European Credit Transfer System grading scale, enacts a population whose performance can be tracked over time. A criteria scale introduces categories describing skills and enacts a moving student subject whose progression can be tracked. This paper argues that the three assessment scales enact the student population as bound to the nation and as simultaneously constituted in relation to transnational European categories and imaginaries of competition. As part of this, this paper discusses how the national test and PISA are used to single out students of non-European background, anticipated to be low PISA achievers and nonparticipants in a European knowledge economy. (shrink)