Stratified random sampling is a method of sampling that divides a population into smaller groups that form the basis of test samples.
Researchers argue that, in some situations where machine-learning models are used to allocate scarce resources or opportunities, randomizing decisions in a structured way may lead to fairer outcomes.
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