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Various attribution methods have been developed to explain deep neuralnetworks (DNNs) by inferring the attribution/importance/contribution score ofeach input variable to the final output. However, existing attribution methodsare often built upon different heuristics. There remains a lack of a unifiedtheoretical understanding of why these methods are effective and how they arerelated. To this end, for the first time, we formulate core mechanisms offourteen attribution methods, which were designed on different heuristics, intothe same mathematical system, i.e., the system of Taylor interactions.Specifically, we prove that attribution scores estimated by fourteenattribution methods can all be reformulated as the weighted sum of two types ofeffects, i.e., independent effects of each individual input variable andinteraction effects between input variables. The essential difference among thefourteen attribution methods mainly lies in the weights of allocating differenteffects. Based on the above findings, we propose three principles for a fairallocation of effects to evaluate the faithfulness of the fourteen attributionmethods.

Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions