Projects

Synthetic Data: Facts, Representations, and Transparency

How is AI-generated synthetic data created and used across domains? This research group investigates its societal impact, examining bias, representation, legitimacy, and trustworthiness. Through critical algorithm studies, computational experiments, and intersectional analysis, we develop tools and vocabularies for transparency, reproducibility, and ethical integration. This is a collaboration between Södertörn, Linköping, Uppsala Universities. Funded by » WASP-HS.

Blind Spots in Pandemic Preparedness: “Pathogen X” and the Development of Epidemic Surveillance after Covid

New surveillance systems aim to detect disease threats before they become pandemics. But do they create blind spots? This project examines which pathogens, populations, and places are prioritized in wastewater analysis, antibiotic resistance tracking, and pathogen identification—and which are excluded. Funded by the » Swedish Research Council.

Pandemic Infrastructures: Preparing for the Wrong Pandemic?

Sweden has invested heavily in technical systems for pandemic preparedness: wastewater analysis, bacterial monitoring, pathogen detection. But these systems embody assumptions about threats, knowledge, and protection. Are we optimizing for the last pandemic while missing the next one? This project studies how these infrastructures actually function in practice. Funded by the » Swedish Research Council.

Social Complexity and Fairness in Synthetic Medical Data

This project explores the use of synthetic data in the medical domain, focusing on issues such as the scarcity of medical data caused by privacy concerns and the challenges of creating representative datasets. The project investigates how social complexity and fairness considerations should inform the generation and use of synthetic medical data.

Principal Investigators: Ericka Johnson (Linköping University), Francis Lee, Gabriel Eilertsen, and Saghi Hajisharif.

Funded by » WASP-HS.

The New Scientific Revolution? Consequences of AI and Big Data in Biomedicine

Today, new digital tools and methods create a growing flood of Big data. In order to manage this growing flood of data, many data-driven research projects in biomedicine are turning to new analytical methods using AI. As a result, we are currently experiencing an explosive introduction of AI in biomedicine. AI seems to promise a whole new way of producing knowledge about the world.

How do actors handle the introduction of AI in the biosciences? What are the debates that AI lead to? Challenges? Dilemmas? Funded by » Marcus & Marianne Wallenbergs stiftelse.

The rise of infodemiology: towards a sociological understanding of Big Data infrastructures for pandemic surveillance

In today’s globalized world, computer systems are increasingly central for the detection of disease outbreaks: they track and analyze data, they alert to unusual events, and they make possible earlier detection of disease outbreaks. However, outbreak surveillance is currently undergoing a momentous shift from traditional epidemiology to “infodemiology”. The word infodemiology points to a transformation where traditional epidemiological assessments are being complemented with new forms of information gathering and computer processing. Funded by the » Swedish Research Council.

Trials of Value: On the valuation practices in designing medical experiments

In this project we focus how scientists in practice answer the question “What knowledge is worth pursuing?”. The purpose of this project is to investigate the practices of research design in biomedical experiments to understand how the economic, scientific, and medical are intertwined in research design. Here we wish to examine the economic as an important part of determining what questions can, and cannot, be addressed in subsequent experiments.

The Trials of value project is a collaborative project with CF Helgesson.

Funded by Riksbankens Jubileumsfond.