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

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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.
Original languageEnglish
Title of host publicationAIES ’23, August 8–10, 2023, Montréal, QC, Canada
Number of pages12
PublisherAssociation for Computing Machinery
Publication date2023
Publication statusPublished - 2023
Event2023 AAAI/ACM Conference on AI, Ethics, and Society - AIES '23 - Montreal, Canada
Duration: 8 Aug 202310 Aug 2023


Conference2023 AAAI/ACM Conference on AI, Ethics, and Society - AIES '23

ID: 362452948