WASP-HS is funding a new research group, Synthetic Data: Facts, Representations, and Transparency. I lead it together with Ericka Johnson at Linköping University and Ylva Söderfeldt at Uppsala University.
The group is funded by the Wallenberg Foundations for five years. Five years is a long time in research, and it gives us room to build something.
What is synthetic data?
Synthetic data is data made by AI models to stand in for real data. Patient records that belong to no patient. Faces that belong to no one. It is used where real data is scarce, private or expensive, and it is spreading fast.
But synthetic data is not just a technical fix. It raises questions about what counts as truth. What does a synthetic data set treat as normal? Who falls outside it? And why should anyone trust data that was, in a sense, made up?
What we will do
We will follow how synthetic data is made and used across fields, and what it does to the knowledge built on it. Bias, representation, legitimacy and trust are at the center. We combine critical algorithm studies, computational experiments and intersectional analysis, and try to build tools and vocabularies for transparency.
The group grows out of an earlier WASP-HS project, Social Complexity and Fairness in Synthetic Medical Data, with Ericka, Gabriel Eilertsen and Saghi Hajisharif. That is where we started thinking about what we call intersectional hallucinations, in The ontological politics of synthetic data.
The group is part of InterSynth Lab, and WASP-HS describes it on its site. More to come.