The increasing digitisation of healthcare services has transformed healthcare provision into a data-centric enterprise. Thinking with Joan Tronto and her notion of care, we study medical data practices in the context of a health-tech company developing an algorithmically driven platform to match patients and their physicians with clinical trials. What does it mean to pose the patient in the centre in such a context? In this paper, we show how the enactments of patient-centrism translate to multidimensional enactments of data care for a diversity of domain experts handling medical data, informed by the values and backgrounds of each 'data handler' situated within the concerns of their domain expertise. Where data experts engage solely with the patients' data to facilitate data creation for the platform's algorithmic system, the quest for data quality depends on the preceding practices of care and affective labour about and for the patients. We show how patients get help to torque their medical records and histories into data to fit the demands of the system to ensure access to experimental treatments and clinical trials. We demonstrate how patient-centrism manifests as care for data quality, shaped throughout by differentiated concerns for regulatory compliance. Finally, we argue that regulatory compliance constitutes a care practice across data work that is diversified in its enactments by the experts' domain concerns and backgrounds.
In 2016, the Norwegian police signed a contract with Palantir Technology for the provision of the surveillance platform Gotham, a platform that enables agencies to integrate data stored in different databases from internal and external sources. The project was to be ready for operation in 2018. In February 2020, the Norwegian Police Directorate announced the project had ended. When they signed off the contract, they had spent around NOK 100 million for nothing. Drawing on documents and interviews with police managers and officers involved in the Gotham project, we explore how platformization represented a conflicting institutional logic which collided with the dominant one and threatened established power structures. Platformization, understood as an organizational process, competes with professionalism and bureaucracy as established institutional logics in the police to control and coordinate work. The analysis of conflicting institutional logics stemming from socio-technical and material objects, reveals how police staff experiences of the symbolic, material and cognitive aspects of logics are of importance in domestication processes. The struggle for market dominance also fueled the conflict between established and new suppliers. The totalizing tendencies of platform policing may explain why the project went on for so long before it was terminated. To a certain degree, our findings show data becoming a major currency, and there is a calculative logic in this type of policing. Platform policing has user activity as its source, and non-use stops the business.
The integration of automated decision-making systems has transformed police work and our understanding of security and surveillance. Despite a growing theoretical literature on shifts in policing due to widespread analytical platform adoption, the public's understanding and perception of these changes are largely unexplored. This study aims to bridge this gap by empirically examining citizens' perspectives on the new dynamics of police work in two societies with varying levels of experience with automation in the public sector: Estonia and Sweden. By combining data from a representative, scenario-based quantitative survey conducted among the general population (n = 2500) and qualitative storytelling techniques implemented in classroom settings with students (n = 23) who take classes with a focus on critical data studies, this research seeks to investigate people's imaginaries, concerns, and expectations regarding predictive policing. The findings shed light on the observation that, in the era of data, the police are not solely perceived as an institution ensuring security or as a source of citizen apprehension related to surveillance. Rather, the transformations in police work are understood as 'distant technologies', wherein individuals, be they, citizens, or police officers, are increasingly removed from the direct application of these technologies. This article uncovers that when citizens possess low levels of trust in the police, the implementation of automation can further exacerbate the disconnect between citizens and the state. Furthermore, this research proposes an innovative approach to studying automated systems by combining scenario-based and storytelling methods, thereby making a valuable contribution to methodologies employed in the study of data.
In an online environment where mis- and disinformation are flourishing, credibility evaluations of online news have become vital. The information processing literature suggests that individuals can either engage in systematic or in heuristic credibility evaluations, but whether this also applies to evaluations of online news and to the adolescent population is still unclear. Using an online survey (N = 338) with Flemish 15-19-year-olds, we examine adolescents' credibility evaluation strategies. Adolescents reported using significantly more systematic evaluations than heuristic evaluations. Moreover, our findings revealed that only systematic evaluations contributed to their perceived credibility evaluation skills. Lastly, media use did not predict their credibility evaluation strategy, and attitudes and knowledge about the news only partially predicted systematic credibility evaluations. These findings are valuable for future research on credibility evaluation strategies and for practitioners and educators focusing on news literacy and credibility evaluation skills.
This article examines how platform-based AI competitions structure power relations in medical imaging research. It focuses on two leading platforms, Kaggle and Grand Challenge, which provide organisational as well as infrastructural support to run AI competitions. In dialogue with critical AI and platform studies research, we investigate how such competitions are organised - under which infrastructural conditions and by whom - and how this shapes processes of model production and evaluation. To address these concerns, we have collected data from 118 medical image AI competitions on Kaggle and Grand Challenge, organised between January 2017 and May 2022. In addition, a variety of platform boundary resources - platform documentation, competition descriptions, dataset descriptions, and competition leaderboards - have been gathered. The analysis of these materials shows, first, that platforms direct the AI development process by requiring substantial financial resources, defining which institutions can host a competition and under which conditions. Second, competition organisers define dataset diversity and the generalisability of models. As most datasets are constructed with data from hospitals in North America, Western Europe and China, the application of models to different geographical contexts is potentially limited. Finally, competition participants influence model development through the institutional, demographic, and disciplinary contexts in which they operate. Overall, the examination demonstrates the importance of critically interrogating the entire medical AI research pipeline, including the definition of research problems, the construction of datasets as well as model production and evaluation.
