Out-of-Distribution (OOD) detection is a critical task in machine learning, particularly in safety-sensitive applications where model failures can have serious consequences. However, current OOD detection methods often suffer from restrictive distributional assumptions, limited scalability, and a lack of interpretability. To address these challenges, we propose STOOD-X, a two-stage methodology that combines a Statistical nonparametric Test for OOD Detection with eXplainability enhancements. In the first stage, STOOD-X uses feature-space distances and a Wilcoxon-Mann-Whitney test to identify OOD samples without assuming a specific feature distribution. In the second stage, it generates user-friendly, concept-based visual explanations that reveal the features driving each decision, aligning with the BLUE XAI paradigm. Through extensive experiments on benchmark datasets and multiple architectures, STOOD-X achieves competitive performance against state-of-the-art post hoc OOD detectors, particularly in high-dimensional and complex settings. In addition, its explainability framework enables human oversight, bias detection, and model debugging, fostering trust and collaboration between humans and AI systems. The STOOD-X methodology therefore offers a robust, explainable, and scalable solution for real-world OOD detection tasks.

本研究解决了当前 OOD 检测方法在假设限制、可扩展性不足和解释性缺乏等方面的挑战。提出的 STOOD-X 方法论结合了统计非参数检验和可解释性增强，通过特征空间距离和 Wilcoxon-Mann-Whitney 测试在没有特定分布假设的情况下识别 OOD 样本，并生成基于概念的用户友好可视化解释。实验证明，该方法在高维复杂设置中表现出色，促进了人机协作与信任。

STOOD-X 方法论：使用统计非参数检验进行 OOD 检测的大规模数据集，增强可解释性