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

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.

References in corpus (5)

• Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
• "Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?
• Computer Vision and Conflicting Values: Describing People with Automated Alt Text
• A Step Toward More Inclusive People Annotations for Fairness
• Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation

Cited by in corpus (4)

• Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
• Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICU
• In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
• "It depends": Configuring AI to Improve Clinical Usefulness Across Contexts