Human Understandable Explanations

The current state of the art in eXplainable AI in most cases only marks salient features in data space. That is, local XAI methods mark the features and feature dimensions most dominantly involved in the decision making of a model per data point. Global XAI approaches conversely aim to identify and visualize the features in the data the model as a whole is most sensitive to. Both approaches work well on e.g., photographic images, where no specialized domain expertise is required for understanding and interpreting features. However, once domain expertise is lacking, the data itself might become hard to understand for humans, or the principle of the model’s information processing is not interpretable, both local and global XAI cease to be informative.