Symbolic vs Black-Box Explanations: A Model-Driven Approach Using Grammatical Evolution

Black-box explainability tools like LIME and SHAP are widely used to interpret machine learning models.However, their post-hoc, local nature often results in inconsistent and semantically opaque explanations.This paper presents a model-driven explainability approach using grammatical evolution (GE), enabling the discovery of symbolic, human-readable models.We compare black-box explanations to symbolic GEgenerated models on two benchmark tasks: a quadratic equation classification problem and the Iris dataset.GE produces interpretable, consistent, and semantically meaningful expressions consistent with domain knowledge, offering a more trustworthy foundation for explainable AI.The use of Meaningful Intermediate Variables (MIVs) further improves the clarity and expressiveness of the symbolic models.