Training Deep Models to be Explained with Fewer Examples
This work addresses the need for more interpretable deep learning models in real-world applications, though it is incremental as it builds on existing example-based explanation methods.
The paper tackles the problem of deep models being difficult to explain with few examples, which can lead to unfaithful explanations, and proposes a method that trains models to be faithfully explained by example-based explanation models with fewer examples, improving faithfulness while maintaining predictive performance as demonstrated in experiments.
Although deep models achieve high predictive performance, it is difficult for humans to understand the predictions they made. Explainability is important for real-world applications to justify their reliability. Many example-based explanation methods have been proposed, such as representer point selection, where an explanation model defined by a set of training examples is used for explaining a prediction model. For improving the interpretability, reducing the number of examples in the explanation model is important. However, the explanations with fewer examples can be unfaithful since it is difficult to approximate prediction models well by such example-based explanation models. The unfaithful explanations mean that the predictions by the explainable model are different from those by the prediction model. We propose a method for training deep models such that their predictions are faithfully explained by explanation models with a small number of examples. We train the prediction and explanation models simultaneously with a sparse regularizer for reducing the number of examples. The proposed method can be incorporated into any neural network-based prediction models. Experiments using several datasets demonstrate that the proposed method improves faithfulness while keeping the predictive performance.