Influence-Driven Explanations for Bayesian Network Classifiers

This can lead to the undesirable of many of its models. We focus on situation where explanations of the outputs from two very explanations for discrete Bayesian network classifiers diverse systems, e.g., a Bayesian network classifier (BC) and a (BCs), targeting greater transparency of their neural model, performing the same task may be identical, despite inner workings by including intermediate variables completely different underpinnings. This trend towards in explanations, rather than just the input and output explanations focusing on inputs and outputs exclusively is variables as is standard practice. The proposed not limited to model-agnostic explanations, however. Explanations influence-driven explanations (IDXs) for BCs are tailored towards specific AI methods are often also systematically generated using the causal relationships restricted to inputs' influence on outputs, e.g., the methods of between variables within the BC, called influences, [Shih et al., 2018] for BCs or of [Bach et al., 2015] for neural which are then categorised by logical requirements, networks. Various methods have been devised for interpreting called relation properties, according to their the intermediate components of neural networks (e.g., behaviour. These relation properties both provide see [Bau et al., 2017]), and [Olah et al., 2018] have shown the guarantees beyond heuristic explanation methods benefits of identifying relations between these components and allow the information underpinning an explanation and inputs/outputs. Some methods exist for accommodating to be tailored to a particular context's and intermediate components in counterfactual explanations user's requirements, e.g., IDXs may be dialectical (CFXs) of BCs [Albini et al., 2020].