Local and Global Interpretability Using Mutual Information in Explainable Artificial Intelligence

Numerous studies have exploited the potential of Artificial Intelligence (AI) and Machine Learning (ML) models to develop intelligent systems in diverse domains for complex tasks, such as analysing data, extracting features, prediction, recommendation etc. However, presently these systems embrace acceptability issues from the end-users. The models deployed at the back of the systems mostly analyse the correlations or dependencies between the input and output to uncover the important characteristics of the input features, but they lack explainability and interpretability that causing the acceptability issues of intelligent systems and raising the research domain of eXplainable Artificial Intelligence (XAI). In this study, to overcome these shortcomings, a hybrid XAI approach is developed to explain an AI/ML model's inference mechanism as well as the final outcome. The overall approach comprises of 1) a convolutional encoder that extracts deep features from the data and computes their relevancy with features extracted using domain knowledge, 2) a model for classifying data points using the features from autoencoder, and 3) a process of explaining the model's working procedure and decisions using mutual information to provide global and local interpretability. To demonstrate and validate the proposed approach, experimentation was performed using an electroencephalography dataset from road safety to classify drivers' in-vehicle mental workload. The outcome of the experiment was found to be promising that produced a Support Vector Machine classifier for mental workload with approximately 89% performance accuracy. Moreover, the proposed approach can also provide an explanation for the classifier model's behaviour and decisions with the combined illustration of Shapely values and mutual information.