CAFE: Conflict-Aware Feature-wise Explanations
Adam Dejl Hamed Ayoobi Matthew Williams Francesca Toni
Feature attribution methods are widely used to explain neural models by
determining the influence of individual input features on the models' outputs.
We propose a novel feature attribution method, CAFE (Conflict-Aware
Feature-wise Explanations), that addresses three limitations of the existing
methods: their disregard for the impact of conflicting features, their lack of
consideration for the influence of bias terms, and an overly high sensitivity
to local variations in the underpinning activation functions. Unlike other
methods, CAFE provides safeguards against overestimating the effects of neuron
inputs and separately traces positive and negative influences of input features
and biases, resulting in enhanced robustness and increased ability to surface
feature conflicts. We show experimentally that CAFE is better able to identify
conflicting features on synthetic tabular data and exhibits the best overall
fidelity on several real-world tabular datasets, while being highly
computationally efficient.