eXplainable AI Narratives
Introduction The field of Explainable Artificial Intelligence (XAI) has flourished, producing a vast array of methods to elucidate the internal mechanisms and decision-making rationales of opaque models. Classical XAI techniques—such as feature attribution, saliency mapping, rule extraction, and counterfactual reasoning—seek to expose the logic underlying a model’s predictions. However, despite their algorithmic sophistication, the outputs of these explainers are often intricate, mathematical, and complex for non-technical stakeholders to interpret. This mismatch between technical transparency and human interpretability remains a central challenge to the practical adoption of explainable AI.
Dec 4, 2025
