A feedback loop in machine learning occurs when a model’s predictions influence the data that is later used to train that model or another model. Such loops can reinforce patterns, amplify bias, or change real-world behavior in ways that affect future model performance.
In machine learning, a situation in which a model's predictions influence the training data for the same model or another model. For example, a model that recommends movies will influence the movies that people see, which will then influence subsequent movie recommendation models. See Production ML systems: Questions to ask in Machine Learning Crash Course for more information.