CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs

TL;DR

CGXplain is a novel rule-based explanation method for deep neural networks that uses dual linear programming to produce stable, aligned, and compact rule sets optimized for multiple objectives.

Contribution

It introduces CGX, a dual linear programming approach that overcomes limitations of existing methods by ensuring alignment, stability, and multi-objective optimization in DNN explanations.

Findings

Reduces rule set size by over 80%

Maintains or improves accuracy and fidelity

Ensures reproducibility and stability of explanations

Abstract

Rule-based surrogate models are an effective and interpretable way to approximate a Deep Neural Network's (DNN) decision boundaries, allowing humans to easily understand deep learning models. Current state-of-the-art decompositional methods, which are those that consider the DNN's latent space to extract more exact rule sets, manage to derive rule sets at high accuracy. However, they a) do not guarantee that the surrogate model has learned from the same variables as the DNN (alignment), b) only allow to optimise for a single objective, such as accuracy, which can result in excessively large rule sets (complexity), and c) use decision tree algorithms as intermediate models, which can result in different explanations for the same DNN (stability). This paper introduces the CGX (Column Generation eXplainer) to address these limitations - a decompositional method using dual linear programming to extract rules from the hidden representations of the DNN. This approach allows to optimise for any number of objectives and empowers users to tweak the explanation model to their needs. We evaluate our results on a wide variety of tasks and show that CGX meets all three criteria, by having exact reproducibility of the explanation model that guarantees stability and reduces the rule set size by >80% (complexity) at equivalent or improved accuracy and fidelity across tasks (alignment).