  Understanding the predictions made by Artificial Intelligence (AI) systems is
becoming more and more important as deep learning models are used for
increasingly complex and high-stakes tasks. Saliency mapping - an easily
interpretable visual attribution method - is one important tool for this, but
existing formulations are limited by either computational cost or architectural
constraints. We therefore propose Hierarchical Perturbation, a very fast and
completely model-agnostic method for explaining model predictions with robust
saliency maps. Using standard benchmarks and datasets, we show that our
saliency maps are of competitive or superior quality to those generated by
existing black-box methods - and are over 20x faster to compute.
