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.

翻译：数据代表性对于通过机器学习模型从数据中推断结论至关重要。学者们日益关注揭示模型中的偏差和公平性问题，同时也关注输入数据中固有的偏差。然而，针对人工智能系统中用于适当推断的样本（数据集）的代表性研究仍然有限。本文回顾了代表性样本的定义和概念，并调查了这些概念在科学人工智能文献中的应用。我们引入了三个可测量的概念，以帮助聚焦这些定义并评估不同数据样本。此外，我们证明，在构建人工智能系统时，覆盖输入空间的代表性样本与模拟目标总体分布的代表性样本之间的对比尤为关键。通过在美国人口普查数据上的实证演示，我们评估了这些概念相互对立的内在属性。最后，我们提出了一个围绕数据代表性创建和记录数据的框架性问题集，作为现有数据集文档模板的补充。