Influence-Driven Explanations for Bayesian Network Classifiers

TL;DR

This paper introduces influence-driven explanations (IDXs) for Bayesian network classifiers, enhancing transparency by systematically incorporating causal influences and logical properties to generate context-aware, interpretable explanations.

Contribution

It presents a novel influence-based explanation framework for Bayesian classifiers that guarantees interpretability and can be tailored to user and context requirements.

Findings

IDXs outperform heuristic methods in explanation quality

IDXs can explain various Bayesian classifier types

The approach integrates and improves upon existing explanation methods

Abstract

One of the most pressing issues in AI in recent years has been the need to address the lack of explainability of many of its models. We focus on explanations for discrete Bayesian network classifiers (BCs), targeting greater transparency of their inner workings by including intermediate variables in explanations, rather than just the input and output variables as is standard practice. The proposed influence-driven explanations (IDXs) for BCs are systematically generated using the causal relationships between variables within the BC, called influences, which are then categorised by logical requirements, called relation properties, according to their behaviour. These relation properties both provide guarantees beyond heuristic explanation methods and allow the information underpinning an explanation to be tailored to a particular context's and user's requirements, e.g., IDXs may be dialectical or counterfactual. We demonstrate IDXs' capability to explain various forms of BCs, e.g., naive or multi-label, binary or categorical, and also integrate recent approaches to explanations for BCs from the literature. We evaluate IDXs with theoretical and empirical analyses, demonstrating their considerable advantages when compared with existing explanation methods.