ShapBPT in Perspective: A Consolidated Review and an eXplainable Anomaly Detection Case Study

As AI-powered vision components are increasingly embedded in production software systems and services: therefore system-level quality, reliability, diagnosability, and accountable decision-making requires explanations that engineers can trust, debug, and operate at scale. Recent research on explainable computer vision has highlighted pixel-level feature attributions as a practical way to expose how visual evidence drives black-box predictions. Hierarchical Shapley explanations built on the Owen coalition formulation offer a principled, model-agnostic foundation, yet existing approaches typically rely on rigid, data-agnostic partitions that overlook the multiscale structure of images, leading to slow convergence and saliency maps that poorly follow true object morphology.We review ShapBPT, a recently published data-aware attribution method that computes hierarchical Shapley coefficients over a Binary Partition Tree (BPT) tailored to the image being explained. By aligning the coalition hierarchy with intrinsic morphological cues and combining it with an adaptive Owen-style recursion, ShapBPT focuses the evaluation budget on semantically coherent regions, yielding crisper explanations while reducing computational overhead. Experiments across multiple computer-vision tasks, datasets, and model families have shown that ShapBPT improves structural alignment and efficiency compared to existing explainers for understanding model decisions.