Explainable AI: Moving from Numbers to Meaningful Insights

Abstract

The increasing complexity of machine learning models has heightened the demand for their explainability. While most presentations at conferences like GHOST Day focus on feature importance, particularly Shapley values, these explanations are often criticized as incomprehensible, even to machine learning experts. In contrast, the XAI 2.0 manifesto advocates for concept-based explanations, such as ia prototypes - representative instances. Besides introducing the problem, the author will explore existing approaches and discuss recent contributions to concept-based explainability, including their own work on prototype-based concept drift detection, which ensures the approach’s intrinsic interpretability. Additionally, the potential of prototypes to improve professional ML applications will be discussed.

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