118 citations · 172 across the 5 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2023★ 6 cited
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models
Daniel G. Krakowczyk, Paul Prasse, David R. Reich +3
Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotr…
cs.LG2022★ 118 cited
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Anna Hedström, Leander Weber, Dilyara Bareeva +5
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelli…