Parameter Averaging for Robust Explainability

Neural Networks are known to be sensitive to initialisation. The explanation methods that rely on neural networks are not robust since they can have variations in their explanations when the model is initialized and trained with different random seeds. The sensitivity to model initialisation is not desirable in many safety critical applications such as disease diagnosis in healthcare, in which the explainability might have a significant impact in helping decision making. In this work, we introduce a novel method based on parameter averaging for robust explainability in tabular data setting, referred as XTab. We first initialize and train multiple instances of a shallow network (referred as local masks) with different random seeds for a downstream task. We then obtain a global mask model by "averaging the parameters" of local masks and show that the global model uses the majority rule to rank features based on their relative importance across all local models. We conduct extensive experiments on a variety of real and synthetic datasets, demonstrating that the proposed method can be used for feature selection as well as to obtain the global feature importance that are not sensitive to sub-optimal model initialisation.