Rectifying disability misrepresentation in synthetic data: Equalise seminar with Jamie Danemayer

How can we improve disability representation in datasets while protecting individual privacy? In this seminar, Jamie Danemayer explores the opportunities and challenges of using synthetic data to address gaps in disability data. The talk examines how underrepresentation in real-world datasets can be carried over into synthetic versions, potentially reinforcing existing inequalities. Drawing on a case study using UK population data, the seminar compares different approaches to modelling disability in synthetic datasets and discusses their implications for research, policy, and data-driven decision-making. The session offers valuable insights into how more inclusive and representative data can support better outcomes for disabled people. Dr Jamie Danemayer is a Research Fellow at the Global Disability Innovation Hub, UCL, with a PhD in Population Data Science from UCL and an MSc in Demography and Health from the London School of Hygiene & Tropical Medicine. Their research focuses on disability data, assistive technology, and population health. Jamie has worked with organisations including the WHO and Asian Development Bank, and has collaborated with governments in England, Sri Lanka, and India on nationally representative surveys measuring access to assistive technology. The seminar was recorded on 22/09/2026. Find out more about our work on https://equalisehealthequity.org/ Follow us: on Linkedin   / esrc-centre-for-lifecourse-health-equity-e...   on Bluesky https://bsky.app/profile/equalise.bsk...