Pathwise Explanation of ReLU Neural Networks
This work addresses the need for transparency in neural networks, particularly for users in fields requiring reliable AI, but it is incremental as it builds on existing linear model approaches.
The paper tackles the problem of explaining ReLU neural networks by introducing a pathwise explanation method that focuses on subsets of hidden units in decision-making paths, resulting in clearer and more consistent explanations that outperform others quantitatively and qualitatively.
Neural networks have demonstrated a wide range of successes, but their ``black box" nature raises concerns about transparency and reliability. Previous research on ReLU networks has sought to unwrap these networks into linear models based on activation states of all hidden units. In this paper, we introduce a novel approach that considers subsets of the hidden units involved in the decision making path. This pathwise explanation provides a clearer and more consistent understanding of the relationship between the input and the decision-making process. Our method also offers flexibility in adjusting the range of explanations within the input, i.e., from an overall attribution input to particular components within the input. Furthermore, it allows for the decomposition of explanations for a given input for more detailed explanations. Experiments demonstrate that our method outperforms others both quantitatively and qualitatively.