What is synthetic data, really? We probe what it means for data to be "real," "generated," or "made up," and how those distinctions shape knowledge claims.
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WASP-HS Research Environment
Synthetic Data.
Synthetic Data.
Real Questions.
The Interdisciplinary Synthetic Data Lab (InterSynth Lab) is an open and interdisciplinary WASP-HS research environment where social scientists and computer scientists investigate how synthetic data is made, represented, and trusted - and what that means for society.
Lab News
Jun 10 Swedish STS Conference 2026: Cross-Pollinations, Contamination, Collaboration Aug 25 Workshop: The Politics of Models – Bias, Representation & Transparency in Generative Models Sep 8 EASST 2026 Panel: Synthetic Data and Representation – The Politics of AI Generated Computational Practices May 28 Virtual Meetup With UK Synthetic Data Researchers May 27 PopVet: Hur påverkar AI-genererad data forskningen? Jun 10 Swedish STS Conference 2026: Cross-Pollinations, Contamination, Collaboration Aug 25 Workshop: The Politics of Models – Bias, Representation & Transparency in Generative Models Sep 8 EASST 2026 Panel: Synthetic Data and Representation – The Politics of AI Generated Computational Practices May 28 Virtual Meetup With UK Synthetic Data Researchers May 27 PopVet: Hur påverkar AI-genererad data forskningen?
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Universities
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Researchers
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Research themes
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Open questions
About
Interdisciplinary by design.
Synthetic data sits at the crossroads of computer science, social science, and the humanities. InterSynth Lab is built to live there — not above it.
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What is synthetic data?
Algorithmically generated data that imitates real-world distributions — used to train models, protect privacy, and fill gaps where real data is scarce or sensitive.
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Why does it matter?
Synthetic data is being deployed in healthcare, finance, and public administration today. The questions of bias, transparency, and accountability are urgent — not theoretical.
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Why interdisciplinary?
Generators are computational, but their consequences are social. We need both lenses — and the conversations between them.
Research
Five themes, one question.
What does it take for synthetic data to be trustworthy?
Synthetic data inherits — and amplifies — the biases of the data and models that produce it. We study representation gaps and intersectional effects in generated datasets.
Explore →When generative models invent people, identities, or facts, who and what is being misrepresented? We map the systematic errors that fall along intersectional axes.
Explore →We design frameworks for documenting, verifying, and auditing synthetic datasets so downstream users can trust what they are working with.
Explore →From healthcare to public administration, synthetic data is being deployed at speed. We examine the practical, legal, and ethical stakes of those deployments.
Explore → People
Led by three PIs across three universities.
Francis Lee
Research Leader
Södertörn University
Investigates the social and cultural dimensions of synthetic data and machine-learned knowledge.
Ericka Johnson
Research Leader
Linköping University
Investigates the WASP-HS research environment exploring synthetic data, facts, representations, and transparency.
Ylva Söderfeldt
Research Leader
Uppsala University
Works at the intersection of history of ideas, medical knowledge, and synthetic data practices.