Kernel dependence measures yield accurate estimates of nonlinear relations between random variables, and they are also endorsed with solid theoretical properties and convergence rates. However, they are hampered by the high computational cost involved, and the interpretability of the measure, which remains hidden behind the implicit feature map. Sensitivity Maps for the Hilbert-Schmidt independence criterion (HSIC) provide a way to explicitly analyze and visualize the relative relevance of both examples and features on the dependence measure.