STOOD-X: Explainable out-of-distribution detection via nonparametric statistical testing on large-scale datasets Sevillano García, Iván Luengo Martín, Julián Herrera Triguero, Francisco Explainable Artificial Intelligence Deep Learning Out-of-distribution Out-of-distribution (OOD) detection is a critical task in machine learning, particularly in safety-sensitive appli cations 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 compared to 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 hu mans and AI systems. Therefore, STOOD-X offers a robust, explainable, and scalable solution for real-world OOD detection tasks 2026-03-04T08:56:49Z 2026-03-04T08:56:49Z 2026-09 journal article Sevillano-García, I., Luengo, J., & Herrera, F. (2026). STOOD-X: Explainable out-of-distribution detection via nonparametric statistical testing on large-scale datasets. Pattern Recognition, 177(113254), 113254. https://doi.org/10.1016/j.patcog.2026.113254 https://hdl.handle.net/10481/111878 10.1016/j.patcog.2026.113254 eng info:eu-repo/grantAgreement/EU/PRTR/TSI-100927-2023-1 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier