12 citations · 25 across the 3 of their papers we have counts for
4 papers
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…
RailGoerl24: Görlitz Rail Test Center CV Dataset 2024
Rustam Tagiew, Ilkay Wunderlich, Mark Sastuba +2
Driverless train operation for open tracks on urban guided transport and mainline railways requires, among other things automatic detection of actual and potential obstacles, espec…
Interpretability is in the eye of the beholder: Human versus artificial classification of image segments generated by humans versus XAI
Romy Müller, Marius Thoß, Julian Ullrich +2
The evaluation of explainable artificial intelligence is challenging, because automated and human-centred metrics of explanation quality may diverge. To clarify their relationship,…
Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type
Romy Müller, Marcel Dürschmidt, Julian Ullrich +3
Deep Learning models like Convolutional Neural Networks (CNN) are powerful image classifiers, but what factors determine whether they attend to similar image areas as humans do? Wh…