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Paper short abstract

From specialist algorithms to LLMs, synthetic data is permeating research ecosystems, even positioned as a national asset in the UK's AI Action Plan. This talk examines interplay between synthetic data/models, researchers, and institutional contexts, examining how these shape epistemic cultures.

Paper long abstract

Artificial Intelligence (AI)-generated synthetic data is permeating science and innovation ecosystems, from shaping the development of novel medical algorithms to forming outputs generated by general-purpose Large Language Models (LLMs). Synthetic data is even positioned as a national asset, as illustrated in the UK government’s 2025 AI Action Plan. This talk examines the interplay between the materiality of synthetic data (and models), researcher perspectives and decision-making, and broader institutional contexts and infrastructures, examining how these co-shape practice and policy.

In my discussion of synthetic data, I draw on findings from a UKRI AI metascience fellowship project investigating how synthetic data is reshaping research practices and cultures, “Synthetic Metascience: Tracing Artificial Intelligence-generated epistemic shifts in research practice and cultures". I expand the concept of AI representation coils (Bennett, Catanzariti and Tollon 2025) to synthetic data practices, examining how values, epistemic assumptions and materials/resources interact to form material and epistemic feedback loops. Specifically, I look at how synthetic data shapes epistemic cultures and knowledge-building practices in medical research, and how medical research epistemologies feed into how synthetic data is used and understood. Crucially, I consider how insights from AI metascience research can be translated into practical tools to inform policy and practice.