  Challenges persist in providing interpretable explanations for neural network
reasoning in explainable AI (xAI). Existing methods like Integrated Gradients
produce noisy maps, and LIME, while intuitive, may deviate from the model's
reasoning. We introduce a framework that uses hierarchical segmentation
techniques for faithful and interpretable explanations of Convolutional Neural
Networks (CNNs). Our method constructs model-based hierarchical segmentations
that maintain the model's reasoning fidelity and allows both human-centric and
model-centric segmentation. This approach offers multiscale explanations,
aiding bias identification and enhancing understanding of neural network
decision-making. Experiments show that our framework, xAiTrees, delivers highly
interpretable and faithful model explanations, not only surpassing traditional
xAI methods but shedding new light on a novel approach to enhancing xAI
interpretability. Code at: https://github.com/CarolMazini/reasoning_with_trees .
