Learning Credible Deep Neural Networks with Rationale Regularization

--Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions. It not only hampers their generalization but also makes them less likely to be trusted by end-users. In pursuit of developing more credible DNNs, in this paper we propose CREX, which encourages DNN models to focus more on evidences that actually matter for the task at hand, and to avoid overfitting to data-dependent bias and artifacts. Specifically, CREX regularizes the training process of DNNs with rationales, i.e., a subset of features highlighted by domain experts as justifications for predictions, to enforce DNNs to generate local explanations that conform with expert rationales. Even when rationales are not available, CREX still could be useful by requiring the generated explanations to be sparse. Experimental results on two text classification datasets demonstrate the increased credibility of DNNs trained with CREX. Comprehensive analysis further shows that while CREX does not always improve prediction accuracy on the held-out test set, it significantly increases DNN accuracy on new and previously unseen data beyond test set, highlighting the advantage of the increased credibility. I NTRODUCTION There has been an increasing interest recently in developing explainable deep neural networks (DNNs) [1]-[4]. To this end, a DNN model should be able to provide intuitive explanations for its predictions. Explainability could shed light into the decision making process of DNNs and thus increase their acceptance by end-users. However, explainability alone is insufficient for DNNs to be credible [5], unless the provided explanations conform with the well-established domain knowledge. That is to say, correct evidences should be adopted by the networks to make predictions. The incredibility issue has been observed in various DNN systems.