Many local post-hoc explainability techniques, such as DeConvNet, Guided Backprop, Layer-wise relevance propagation, and integrated gradients, rely on “gradient-like” computations, where explanations are propagated backwards through Neural Networks, one layer at a time. One can alter this backward computation to include attentions, which guides the explanation techniques to produce better explanations.