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- URN to cite this document:
- urn:nbn:de:bvb:355-epub-594698
- DOI to cite this document:
- 10.5283/epub.59469
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Abstract
Artificial Intelligence (AI) and especially Machine Learning (ML) models are ubiquitous in research, business and society. However, the predictions of many ML models are often not transparent for users due to their black box nature. Therefore, several Explainable AI (XAI) methods aiming to provide local explanations for individual ML model predictions have been proposed. Importantly, existing XAI ...

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