Description: External validity refers to the extent to which the findings of an artificial intelligence (AI) model can be generalized to other contexts, populations, or situations different from those originally studied. This concept is crucial in the field of AI, as it allows for the evaluation of the applicability of results obtained in a controlled environment to real-world scenarios. External validity focuses on a model’s ability to provide accurate and relevant predictions outside the specific conditions in which it was trained. This means that a model must not only be effective on the training dataset but also demonstrate its usefulness and effectiveness in diverse situations, which is fundamental for trust and acceptance of AI in practical applications. External validity is influenced by factors such as the representativeness of the training data, the complexity of the model, and the variability of the contexts in which it is applied. In summary, external validity is an essential pillar to ensure that AI models are robust and useful across a variety of situations, which in turn fosters their adoption in different sectors and applications.
