DECODE: Decision Tree Capturing Opaque Decisions via Coverage-Driven Synthetic Sampling
In the context of smart manufacturing, Explainable AI has emerged as an essential solution to ensure trust in complex Machine Learning model decisions. However, most employed methods are limited to feature relevance scoring, lacking in providing a human-interpretable description of model behaviour. Surrogate models, however, address this gap by approximating a complex predictor through an interpretable model, with treebased surrogates striking a great balance between performance, interpretability, and deployability. In this paper, we propose DECODE, a tree-based surrogate framework combined with an Empty Space Search (ESS) synthetic sampling strategy. Unlike commonly employed synthetic sampling approaches that are either contrained to a specific data distribution or focused on local decision-boundary neighbourhoods, ESS aims to maximise data coverage across a dataset's feature range, enabling the surrogate to characterise model behaviour in low-density regions that reflect rare or abnormal conditions. Experiments on multiple low-dimensional datasets and with multiple classifiers show that an ESS-trained DECODE model substantially improves model behaviour explainability, measured through fidelity and rule overlap metrics, while maintaining satisfactory predictive performance with its teacher model. These results highlight the importance of coverage-centred synthetic training to achieve faithful surrogate explanations that go beyond high density data regimes.