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As machine learning models become more accurate, they typically become morecomplex and uninterpretable by humans. The black-box character of these modelsholds back its acceptance in practice, especially in high-risk domains wherethe consequences of failure could be catastrophic such as health-care ordefense. Providing understandable and useful explanations behind ML models orpredictions can increase the trust of the user. Example-based reasoning, whichentails leveraging previous experience with analogous tasks to make a decision,is a well known strategy for problem solving and justification. This workpresents a new explanation extraction method called LEAFAGE, for a predictionmade by any black-box ML model. The explanation consists of the visualizationof similar examples from the training set and the importance of each feature.Moreover, these explanations are contrastive which aims to take theexpectations of the user into account. LEAFAGE is evaluated in terms offidelity to the underlying black-box model and usefulness to the user. Theresults showed that LEAFAGE performs overall better than the currentstate-of-the-art method LIME in terms of fidelity, on ML models with non-lineardecision boundary. A user-study was conducted which focused on revealing thedifferences between example-based and feature importance-based explanations. Itshowed that example-based explanations performed significantly better thanfeature importance-based explanation, in terms of perceived transparency,information sufficiency, competence and confidence. Counter-intuitively, whenthe gained knowledge of the participants was tested, it showed that theylearned less about the black-box model after seeing a feature importance-basedexplanation than seeing no explanation at all. The participants found featureimportance-based explanation vague and hard to generalize it to otherinstances.

LEAFAGE: Example-based and Feature importance-based Explanationsfor Black-box ML models