Testing the diversity and representativeness of datasets used to train machine learning and AI models: verification of coverage for use cases, populations, scenarios, and operational conditions, identification of systematic biases, gaps in data distribution, under-representation of groups or contexts, and validation of model robustness on diversified inputs and edge cases.