Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI

TL;DR

This paper investigates the often-overlooked foundational work in designing ground truth schemas for medical datasets, highlighting external and internal factors that influence data creation and quality for responsible AI.

Contribution

It identifies and analyzes five key factors affecting the creation of medical datasets, emphasizing the importance of ground truth schema design in responsible AI development.

Findings

External factors include regulatory constraints, context, and pressures.

Internal factors involve epistemic differences and labeling limits.

These factors shape data collection and schema design.

Abstract

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.