Comparing Techniques for Temporal Explainable Artificial Intelligence

Artificial Intelligence models have been employed in various fields, leading to a growing interest in the subject and in the development of the models. The direct involvement of complex AI models in decision-making processes stressed the needs to explain the rationales behind the results, globally and locally for each prediction/result via eXplainable Artificial Intelligence (XAI) techniques. This paper compared three XAI techniques (SHAP, LIME and IG) with aim of using them for temporal explainability of predictive results regarding time-series in order to understand if these methods are able provide temporal explanation of deep learning AI models. The comparison provided has been qualitative and quantitative and addressing computational performance. This work has been partially supported by the CN MOST, national center on sustainable mobility in Italy, on CAI4DSA of FAIR, and has been developed on the Snap4City platform.