Can I trust you more? Model-Agnostic Hierarchical Explanations

Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mah'e, a novel approach to provide Model-agnostic hierarchical 'explanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances. Specifically, Mah'e provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing context-dependent interactions to explain global behaviors. Experimental results show that Mah'e obtains improved local interaction interpretations over state-of-the-art methods and successfully explains interactions that are context-free.