Layer-wise Relevance Propagation (LRP) is a technique used to interpret the decisions made by neural networks by backpropagating the prediction score through the network layers to attribute relevance to each input feature. It helps in understanding which parts of the input data contribute most to the output, providing insights into the model's decision-making process and enhancing transparency in AI systems.