Example Concept: AI models, such as neural networks, learn by adjusting weights based on training data. If the data is biased, incomplete, or not representative of real-world scenarios, the model may not generalize well, leading to incorrect predictions. Additionally, models can overfit to training data, capturing noise instead of the underlying pattern, which also results in errors when faced with new data.