Can Improved Data Representation Support AI for Health Equity? A Visual Approach

Representation is a critical concern in the development, use and decisions about AI. AI fairness and health equity research present frameworks for representation that highlight underlying structural issues (such as systemic discrimination) that go beyond, yet also shape, both data and AI tooling. Still, there is a lack of actionable methods to interrogate data representation and support critical reflection on its role in AI systems. We propose a visual, data-driven approach that links decisions about data representation to AI model performance across subgroups. Our method consists of two plots: the representation association plot, which shows whether subgroup representation affects performance, and the representation expansion plot, which simulates how performance disparities may change when expanding subgroup data. We apply our approach to the Lifelines Cohort Study for two health equity use cases: early detection of diabetes and cardiovascular disease. The plots reveal that improving age representation may reduce disparities, while sex and education-based disparities appear unrelated to representation in the dataset. This approach has the potential to guide researchers in identifying when improving data representation may contribute to reducing performance disparities, and when such efforts are unlikely to be effective. As a critical but partial tool, our approach should be embedded in broader inclusive research practices, where representation extends to who defines the data and determines whether and how AI is applied. Future research should validate the actionability of insights with users and priorities of those bearing the health burden.