Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI
This addresses the challenge of ensuring high-quality data for responsible AI in the medical domain, highlighting often-overlooked pre-annotation constraints.
The paper tackles the problem of designing ground truth schemas for medical AI datasets, identifying five external and internal factors that constrain dataset creation before annotation, based on work in three health-tech organizations.
One of the core goals of responsible AI development is ensuring high-quality training datasets. Many researchers have pointed to the importance of the annotation step in the creation of high-quality data, but less attention has been paid to the work that enables data annotation. We define this work as the design of ground truth schema and explore the challenges involved in the creation of datasets in the medical domain even before any annotations are made. Based on extensive work in three health-tech organisations, we describe five external and internal factors that condition medical dataset creation processes. Three external factors include regulatory constraints, the context of creation and use, and commercial and operational pressures. These factors condition medical data collection and shape the ground truth schema design. Two internal factors include epistemic differences and limits of labelling. These directly shape the design of the ground truth schema. Discussions of what constitutes high-quality data need to pay attention to the factors that shape and constrain what is possible to be created, to ensure responsible AI design.