The Proofpoint team worked on understanding and creating user-friendly explanations for black-box classifiers such as random forests and recurrent neural networks. The resulting explanations have robust theoretical foundations and can be generated efficiently. The team also created appropriate visualizations to complement the explanations so that even non-technical analysts can understand and explain why a black-box classifier arrived at a certain decision.