Synthetic Data: Facts, Representations, and Transparency
Synthetic data, computer‑made versions of real information, are becoming more common because they can be used to avoid some of the ethical and practical problems of using real-life data. But these AI‑generated versions of the world raise big questions about what counts as truth, how well they represent reality, and how transparent they should be.
How is synthetic data created and used, and how does this affect bias and representation? How do we know if synthetic data can be trusted? And what tools do we need to make synthetic data clear, transparent, and easy to check?
These are some of the questions explored by researchers in the WASP‑HS research environment Synthetic Data: Facts, Representations, and Transparency.
Research Leaders
Members
Md Fahim Sikder
Linköping University
Vera Danilova
Uppsala University
Postdocs and PhD Students
Alicja Ostrowska
MIT Massachusetts Institute of Technology,
Linköping University,
Södertörn University
Svea Kiesewetter
Södertörn University
Events
Selected Publications
Lee, F., Hajisharif, S., & Johnson, E. (2025). The ontological politics of synthetic data: Normalities, outliers, and intersectional hallucinations. Big Data & Society, 12(2). https://doi.org/10.1177/20539517251318289
Johnson, E., & Hajisharif, S. (2024). The intersectional hallucinations of synthetic data. AI & SOCIETY. https://doi.org/10.1007/s00146-024-02017-8
