VitrAI -- Applying Explainable AI in the Real World
This work addresses the need for practical evaluation of XAI methods for users in real-world applications, but it is incremental as it focuses on demonstration and qualitative assessment rather than novel breakthroughs.
The authors tackled the problem of evaluating explainable AI (XAI) methods in real-world scenarios by developing VitrAI, a web-based service that demonstrates four XAI algorithms across three practical use cases, revealing obstacles and providing qualitative performance estimates.
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.