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Data representativity is crucial when drawing inference from data throughmachine learning models. Scholars have increased focus on unraveling the biasand fairness in models, also in relation to inherent biases in the input data.However, limited work exists on the representativity of samples (datasets) forappropriate inference in AI systems. This paper reviews definitions and notionsof a representative sample and surveys their use in scientific AI literature.We introduce three measurable concepts to help focus the notions and evaluatedifferent data samples. Furthermore, we demonstrate that the contrast between arepresentative sample in the sense of coverage of the input space, versus arepresentative sample mimicking the distribution of the target population is ofparticular relevance when building AI systems. Through empirical demonstrationson US Census data, we evaluate the opposing inherent qualities of theseconcepts. Finally, we propose a framework of questions for creating anddocumenting data with data representativity in mind, as an addition to existingdataset documentation templates.

Data Representativity for Machine Learning and AI Systems