  Machine learning algorithms generally suffer from a problem of
explainability. Given a classification result from a model, it is typically
hard to determine what caused the decision to be made, and to give an
informative explanation. We explore a new method of generating counterfactual
explanations, which instead of explaining why a particular classification was
made explain how a different outcome can be achieved. This gives the recipients
of the explanation a better way to understand the outcome, and provides an
actionable suggestion. We show that the introduced method of Constrained
Adversarial Examples (CADEX) can be used in real world applications, and yields
explanations which incorporate business or domain constraints such as handling
categorical attributes and range constraints.
