31 citations · 36 across the 4 of their papers we have counts for
4 papers
CFIRE: A General Method for Combining Local Explanations
Sebastian Müller, Vanessa Toborek, Tamás Horváth +1
We propose a novel eXplainable AI algorithm to compute faithful, easy-to-understand, and complete global decision rules from local explanations for tabular data by combining XAI me…
Immersive Explainability: Visualizing Robot Navigation Decisions through XAI Semantic Scene Projections in Virtual Reality
Jorge de Heuvel, Sebastian Müller, Marlene Wessels +3
End-to-end robot policies achieve high performance through neural networks trained via reinforcement learning (RL). Yet, their black box nature and abstract reasoning pose challeng…
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning
Sebastian Müller, Vanessa Toborek, Katharina Beckh +3
The Rashomon Effect describes the following phenomenon: for a given dataset there may exist many models with equally good performance but with different solution strategies. The Ra…
Explainable Machine Learning with Prior Knowledge: An Overview
Katharina Beckh, Sebastian Müller, Matthias Jakobs +6
This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elic…