Programs as Black-Box Explanations

22 Nov 2016Sameer SinghMarco Tulio RibeiroCarlos Guestrin

Recent work in model-agnostic explanations of black-box machine learning has demonstrated that interpretability of complex models does not have to come at the cost of accuracy or model flexibility. However, it is not clear what kind of explanations, such as linear models, decision trees, and rule lists, are the appropriate family to consider, and different tasks and models may benefit from different kinds of explanations... (read more)

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