Neural Network Based Identification of Terrorist Groups Using Explainable Artificial Intelligence

Currently machine learning (ML) and artificial intelligence (AI) are applied in diverse domains and governments believe these technologies can be applied in identifying terrorist groups.This paper reviews literature covering the varied use of ML algorithms and proposes a novel approach with a first-time application of a deep neural network (DNN) to an existing case study for the identification of terrorist groups. The research will further seek to explain how the neural network arrived at its decision using SHapley Additive exPlanations (SHAP).The results reveal DNN outperformed two benchmark models in terms of accuracy. SHAP was able to explain features that influenced the predicted results.