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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| Главные авторы: | , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Nature Portfolio
2026-03-01
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| Серии: | Scientific Reports |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1038/s41598-026-37070-4 |
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