Jerone Andrews, Dora Zhao, Orestis Papakyriakopoulos and Alice Xiang won a best paper award at the International Conference on Machine Learning (ICML) for their position paper Measure Dataset Diversity. We spoke to Jerone about the team's methodology, and how they developed a framework for conceptualising, operationalising, and evaluating diversity in machine learning datasets. In our paper, we propose using measurement theory from the social sciences as a framework to improve the collection and evaluation of diverse machine learning datasets. Measurement theory offers a systematic and scientifically grounded approach to developing precise numerical representations of complex and abstract concepts, making it particularly suitable for tasks like conceptualising, operationalising, and evaluating qualities such as diversity in datasets. This framework can also be applied to other constructs like bias or difficulty.