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