Feedback Loop

describes the process of leveraging the output of an AI system and corresponding end-user actions in order to retrain and improve models over time. The AI-generated output (predictions or recommendations) are compared against the final decision (for example, to perform work or not) and provides feedback to the model, allowing it to learn from its mistakes.