  Explainable Artificial Intelligence (XAI) is a pivotal research domain aimed
at understanding the operational mechanisms of AI systems, particularly those
considered ``black boxes'' due to their complex, opaque nature. XAI seeks to
make these AI systems more understandable and trustworthy, providing insight
into their decision-making processes. By producing clear and comprehensible
explanations, XAI enables users, practitioners, and stakeholders to trust a
model's decisions. This work analyses the value of data morphology strategies
in generating counterfactual explanations. It introduces the Overlap Number of
Balls Model-Agnostic CounterFactuals (ONB-MACF) method, a model-agnostic
counterfactual generator that leverages data morphology to estimate a model's
decision boundaries. The ONB-MACF method constructs hyperspheres in the data
space whose covered points share a class, mapping the decision boundary.
Counterfactuals are then generated by incrementally adjusting an instance's
attributes towards the nearest alternate-class hypersphere, crossing the
decision boundary with minimal modifications. By design, the ONB-MACF method
generates feasible and sparse counterfactuals that follow the data
distribution. Our comprehensive benchmark from a double perspective
(quantitative and qualitative) shows that the ONB-MACF method outperforms
existing state-of-the-art counterfactual generation methods across multiple
quality metrics on diverse tabular datasets. This supports our hypothesis,
showcasing the potential of data-morphology-based explainability strategies for
trustworthy AI.
