How can enterprises trust AI-generated explanations when it is nearly impossible for a human to understand how deep neural networks make decisions? To date, there’s been limited assessment of explainability methods within the nascent deep learning field, and most existing evaluations focus on subjective visual interpretations. This talk explores a machine-centric strategy for quantifying the performance of explainability methods on deep neural networks.