Backpropagation is a key algorithm used in training artificial neural networks. It helps the network learn by adjusting its weights based on the error of its predictions. When the network makes a prediction, it compares the result to the actual outcome, calculating the error. This error is then sent backward through the network, layer by layer, to update the weights, minimizing the error in future predictions. The process involves using the chain rule from calculus, which allows the algorithm to determine how much each weight contributed to the overall error. By repeatedly applying backpropagation during training, the network becomes better at recognizing patterns and making accurate predictions.