Explainable Spatio-Temporal Graph Modeling

Abstract

Explainable AI (XAI) focuses on designing inference explanation methods and tools to complement machine learning and black-box deep learning models. Such capabilities are crucially important with the rising adoption of AI models in real-world applications, which require domain experts to understand how model predictions are extracted in order to make informed decisions. Despite the increasing number of XAI approaches for tabular, image, and graph data, their effectiveness in contexts with a spatial and temporal dimension is rather limited. As a result, available methods do not properly explain predictive models’ inferences when dealing with spatio-temporal data. In this paper, we fill this gap proposing a XAI method that focuses on spatio-temporal geo-distributed sensor network data, where observations are collected at regular time intervals and at different locations. Our model-agnostic method performs perturbations on the feature space of the data to uncover relevant factors that influence model predictions, and generates explanations for multiple analytical views, such as features, timesteps, and node location. Our qualitative and quantitative experiments with real-world forecasting datasets show the effectiveness of the proposed method in providing valuable explanations of model predictions.