ExpDNN: Explainable Deep Neural Network
This addresses the need for interpretability in deep learning for users in fields requiring transparent AI, but it appears incremental as it builds on linear regression concepts.
The authors tackled the problem of deep neural networks lacking explainability, especially with input interactions, by proposing ExpDNN with explainable layers, and results showed that absolute weights in these layers can explain input contributions for feature extraction.
In recent years, deep neural networks have been applied to obtain high performance of prediction, classification, and pattern recognition. However, the weights in these deep neural networks are difficult to be explained. Although a linear regression method can provide explainable results, the method is not suitable in the case of input interaction. Therefore, an explainable deep neural network (ExpDNN) with explainable layers is proposed to obtain explainable results in the case of input interaction. Three cases were given to evaluate the proposed ExpDNN, and the results showed that the absolute value of weight in an explainable layer can be used to explain the weight of corresponding input for feature extraction.