The biennial Swedish STS conference, hosted by Malmö and Lund Universities, exploring how collaborations across disciplinary, cultural, and institutional boundaries can address pressing global challenges. Several lab members will attend.
Details →A WASP/WASP-HS workshop bringing together researchers to examine how generative AI models shape knowledge representation, power, and culture. Co-organized by Fahim Sikder and Francis Lee, together with Kathlén Kohn and Kıvanç Tatar.
Details →A panel at the EASST 2026 conference in Krakow bringing together researchers from Sweden, Italy, the UK, Norway, and Denmark to explore the ontological politics of AI and synthetic data. What does the rise of AI-generated data mean for knowledge production, representation, and who—or what—gets to count as real?
Details →Virtual meetup between UK synthetic data researchers and the InterSynth Lab. Contact [email protected] if you want to join.
A lecture exploring how AI-generated datasets are reshaping modern research — opportunities and risks when synthetic models replace real observations. Free admission, no registration required.
Details →How might historians and literary scholars engage with synthetic data—AI-generated text, images, and metadata—while preserving critical, interpretive, and ethical reflection?
Details →Ericka Johnson presented at the Data Innovation Summit 2026 in Stockholm, challenging how organizations think about and validate AI-generated synthetic data.
Details →An opening seminar introducing the InterSynth Lab, its members, and our research agenda on synthetic data.
Francis Lee and Katherine Harrison organized a panel at the 4S annual meeting in Seattle examining how AI-generated synthetic data reshapes knowledge, infrastructure, and lived experience.
Details →A hands-on workshop bringing together researchers from humanities, social sciences, machine learning, and open science to critically examine AI-generated synthetic data.
A workshop at Aarhus University examining synthetic data through the dual lens of representation and representativeness — how diversity issues are handled in AI-generated datasets.
Details →