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Recent years have seen a boom in interest in machine learning systems thatcan provide a human-understandable rationale for their predictions ordecisions. However, exactly what kinds of explanation are trulyhuman-interpretable remains poorly understood. This work advances ourunderstanding of what makes explanations interpretable in the specific contextof verification. Suppose we have a machine learning system that predicts X, andwe provide rationale for this prediction X. Given an input, an explanation, andan output, is the output consistent with the input and the supposed rationale?Via a series of user-studies, we identify what kinds of increases in complexityhave the greatest effect on the time it takes for humans to verify therationale, and which seem relatively insensitive.

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation