Weak Supervision refers to a machine learning approach that trains AI systems using incomplete, noisy, or approximate labels instead of requiring perfect, hand-labeled examples for every piece of training data. This method allows developers to create AI systems when getting high-quality labeled data is expensive, time-consuming, or impossible, by using techniques like automated labeling rules,