Interpretation drift in explainable AI under label noise
Abstract The comprehensibility and human interpretation of classification models are crucial in many applications, such as decision support systems and knowledge discovery, where explanations drive action. However, the presence of class label noise, widespread in real-life data, can significantly im...
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| Autores principales: | , , |
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| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Nature Portfolio
2026-03-01
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| Colección: | Scientific Reports |
| Materias: | |
| Acceso en línea: | https://doi.org/10.1038/s41598-026-37070-4 |
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