Obtaining Example-Based Explanations from Deep Neural Networks
This provides a novel explanation method for deep learning models, addressing interpretability issues in cases where features are not easily interpretable, though it is incremental as it extends existing example-based techniques to DNNs.
The paper tackles the problem of generating example-based explanations for deep neural networks by proposing EBE-DNN, which uses embeddings from a DNN with a k-nearest neighbor classifier to derive attributions, achieving highly concentrated explanations without accuracy loss compared to the original DNN.
Most techniques for explainable machine learning focus on feature attribution, i.e., values are assigned to the features such that their sum equals the prediction. Example attribution is another form of explanation that assigns weights to the training examples, such that their scalar product with the labels equals the prediction. The latter may provide valuable complementary information to feature attribution, in particular in cases where the features are not easily interpretable. Current example-based explanation techniques have targeted a few model types only, such as k-nearest neighbors and random forests. In this work, a technique for obtaining example-based explanations from deep neural networks (EBE-DNN) is proposed. The basic idea is to use the deep neural network to obtain an embedding, which is employed by a k-nearest neighbor classifier to form a prediction; the example attribution can hence straightforwardly be derived from the latter. Results from an empirical investigation show that EBE-DNN can provide highly concentrated example attributions, i.e., the predictions can be explained with few training examples, without reducing accuracy compared to the original deep neural network. Another important finding from the empirical investigation is that the choice of layer to use for the embeddings may have a large impact on the resulting accuracy.