Explainable Artificial Intelligence (XAI) algorithms aim to clarify machine learning (ML) models and predictions. However, the paper argues that the goals and capabilities of XAI are poorly understood due to a flawed reasoning scheme in the literature. This scheme assumes that XAI algorithms directly contribute to goals like trust by providing capabilities like interpretability, which lack precise definitions and clear links to goals. The authors propose a user-centric perspective, focusing on the question: "How can one represent an ML model as a simple function that uses interpreted attributes?" They identify two key challenges for XAI: approximation (representing complex models with simpler ones) and translation (mapping technical attributes to interpretable ones). The paper concludes by reviewing the current state of XAI algorithms in addressing these challenges and advocating for a more realistic approach to describing XAI's capabilities.

Moritz Renftle, Holger Trittenbach, Michael Poznic, Reinhard Heil