1 citations · 1 across the 2 of their papers we have counts for
4 papers
Right for the Wrong Reasons? Evaluating Plausibility and Behavioral Change in Explanation-Guided Learning
David Dembinsky, Adriano Lucieri, Andreas Dengel
Explanation-Guided Learning (EGL) promises models that are not only accurate but also rely on task-relevant evidence. However, whether improved agreement between model explanations…
Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance
Marco Zullich, Emily Schiller, Ivan Gentile +4
Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of their algorithms more human-u…
The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods
Kevin Iselborn, David Dembinsky, Adriano Lucieri +1
The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulat…
Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
David Dembinsky, Adriano Lucieri, Stanislav Frolov +3
Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned paramet…