Description: Sampling bias is a statistical error that occurs when a sample selected for a study does not adequately represent the population of interest. This phenomenon can lead to erroneous conclusions and inappropriate generalization of results. In the context of artificial intelligence (AI), sampling bias becomes a critical issue, as machine learning algorithms rely on representative data for training. If the training data is biased, the resulting models may perpetuate or even amplify those biases, affecting the fairness and accuracy of automated decisions. Key characteristics of sampling bias include inadequate sample selection, lack of diversity in data, and underrepresentation of certain groups. The relevance of this bias is evident in various applications that impact people’s lives, such as facial recognition systems, hiring processes, and credit decisions, where bias in data can lead to discrimination and inequality. Therefore, it is essential to address sampling bias in the development of AI technologies to ensure fair and equitable outcomes.
