VitrAI -- Applying Explainable AI in the Real World
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Abstract (arXiv)
With recent progress in the field of Explainable Artificial Intelligence (XAI) and increasing use in practice, the need for an evaluation of different XAI methods and their explanation quality in practical usage scenarios arises. For this purpose, we present VitrAI, which is a web-based service with the goal of uniformly demonstrating four different XAI algorithms in the context of three real life scenarios and evaluating their performance and comprehensibility for humans. This work reveals practical obstacles when adopting XAI methods and gives qualitative estimates on how well different approaches perform in said scenarios.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 12 Feb 2021.
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20 of 30 references.
References · 20
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- 2017Interpretable Convolutional Neural Networkscited by 162
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