Backpropagation is a key algorithm used in training artificial neural networks. It works by calculating the gradient of the loss function, which measures how far the network's predictions are from the actual results. This gradient is then used to update the weights of the network, allowing it to learn from its mistakes and improve its performance over time. The process involves two main steps: a forward pass, where input data is fed through the network to generate predictions, and a backward pass, where the error is propagated back through the network. This helps adjust the weights in a way that minimizes the error in future predictions.