Can I trust you more? Model-Agnostic Hierarchical Explanations

Understand

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.