Abstract:
Technological advances have integrated artificial intelligence (AI) into various scientific fields, necessitating understanding AI-derived decisions. The field of explainable artificial intelligence (XAI) has emerged to address transparency concerns, offering both transparent models and post-hoc explanation techniques. Recent research emphasises the importance of developing transparent models, with a focus on enhancing the interpretability of these models. An example of a transparent model that would benefit from enhanced post-hoc explainability is Bayesian networks. This research investigates the current state of explainability in Bayesian networks. Literature includes three categories of explanation: explanation of the model, reasoning, and evidence. Drawing upon these categories, we formulate a taxonomy of explainable Bayesian networks. Following this, we extend the taxonomy to include explanation of decisions, an area recognised as neglected within the broader XAI research field. This includes using the same-decision probability, a threshold-based confidence measure, as a stopping and selection criteria for decision-making. Additionally, acknowledging computational efficiency as a concern in XAI, we introduce an approximate forward-gLasso algorithm as a solution for efficiently solving the most relevant explanation. We compare the proposed algorithm with a local, exhaustive forward search. The forward-gLasso algorithm demonstrates accuracy comparable to the forward search while reducing the average neighbourhood size, leading to computationally efficient explanations. All coding was done in R, building on existing packages for Bayesian networks. As a result, we develop an open-source R package capable of generating explanations of evidence for Bayesian networks. Lastly, we demonstrate the practical insights gained from applying post-hoc explanations on real-world data, such as the South African Victims of Crime Survey 2016 - 2017.
