These automated decision frameworks have demonstrated various unwanted consequences as a result of biased data [11, 66-68, 84, 86, 109]. Oftentimes these systems are trained on samples (datasets) from a larger population. Biased results can arise if the sample does not accurately represent the target population, or if there is a lack of sufficient representation for subgroups within the data. While the literature of data bias in machine Learning and artificial intelligence (AI) systems is rich [99], there exists only limited work on the connections between data representativity and AI systems. Terms like representative sample are used ubiquitously in the literature, often without further specification on the details or effects of this representativity. This paper analyzes and surveys data representativity in scientific literature relating to machine learning and AI systems by investigating how different notions of representativity are used and what effects adhering to different notions of data representativity has in relation to appropriate inference. The term representative sample is an overloaded term and a generally accepted definition of what constitutes a representative sample (subset of observations) is hard to find in the literature. A few examples demonstrate that at least a couple of definitions of representative sample exist. The most general definition we found is from D'Excelle (2014) and states ""Representative sampling" is a type of statistical sampling that allows us to use data from a sample to make conclusions that are representative for the population from which the sample is taken."

Data representativity is crucial when drawing inference from data through machine learning models. Scholars have increased focus on unraveling the bias and fairness in the 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 analyzes data representativity in scientific literature related to AI and sampling, and gives a brief overview of statistical sampling methodology from disciplines like sampling of physical materials, experimental design, survey analysis, and observational studies. Different notions of a 'representative sample' exist in past and present literature. In particular, the contrast between the notion of a representative sample in the sense of coverage of the input space, versus a representative sample as a miniature of the target population is of relevance when building AI systems. Using empirical demonstrations on US Census data, we demonstrate that the first is useful for providing equality and demographic parity, and is more robust to distribution shifts, whereas the latter notion is useful in situations where the purpose is to make historical inference or draw inference about the underlying population in general, or make better predictions for the majority in the underlying population. We propose a framework of questions for creating and documenting data, with data representativity in mind, as an addition to existing datasheets for datasets. Finally, we will also like to call for caution of implicit, in addition to explicit, use of a notion of data representativeness without specific clarification.