  The recent years have witnessed the rise of accurate but obscure decision
systems which hide the logic of their internal decision processes to the users.
The lack of explanations for the decisions of black box systems is a key
ethical issue, and a limitation to the adoption of machine learning components
in socially sensitive and safety-critical contexts. %Therefore, we need
explanations that reveals the reasons why a predictor takes a certain decision.
In this paper we focus on the problem of black box outcome explanation, i.e.,
explaining the reasons of the decision taken on a specific instance. We propose
LORE, an agnostic method able to provide interpretable and faithful
explanations. LORE first leans a local interpretable predictor on a synthetic
neighborhood generated by a genetic algorithm. Then it derives from the logic
of the local interpretable predictor a meaningful explanation consisting of: a
decision rule, which explains the reasons of the decision; and a set of
counterfactual rules, suggesting the changes in the instance's features that
lead to a different outcome. Wide experiments show that LORE outperforms
existing methods and baselines both in the quality of explanations and in the
accuracy in mimicking the black box.
