Interpretable Mixture of Experts for Structured Data

With the growth of machine learning for structured data, the need for reliable model explanations is essential, especially in high-stakes applications. We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretability for structured data while preserving accuracy. IME consists of an assignment module and a mixture of interpretable experts such as linear models where each sample is assigned to a single interpretable expert. This results in an inherently-interpretable architecture where the explanations produced by IME are the exact descriptions of how the prediction is computed. In addition to constituting a standalone inherently-interpretable architecture, an additional IME capability is that it can be integrated with existing Deep Neural Networks (DNNs) to offer interpretability to a subset of samples while maintaining the accuracy of the DNNs. Experiments on various structured datasets demonstrate that IME is more accurate than a single interpretable model and performs comparably to existing state-of-the-art deep learning models in terms of accuracy while providing faithful explanations.