The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research. - Thomas Decker 004; Ralf Gross; Alexander Koebler; Michael Lebacher; Ronald Schnitzer; Stefan H. Weber (HCI (40), 2023)

Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning. - Thomas Decker 004; Michael Lebacher; Volker Tresp (ICASSP, 2023)

Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts. - Thomas Decker 004; Michael Lebacher; Volker Tresp (INDIN, 2023)

Towards Scenario-Based Safety Validation for Autonomous Trains with Deep Generative Models. - Thomas Decker 004; Ananta R. Bhattarai; Michael Lebacher (SAFECOMP, 2023)

Provably Better Explanations with Optimized Aggregation of Feature Attributions. - Thomas Decker 004; Ananta R. Bhattarai; Jindong Gu; Volker Tresp; Florian Buettner 001 (ICML, 2024)

Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance. - Thomas Decker 004; Alexander Koebler; Michael Lebacher; Ingo Thon; Volker Tresp; Florian Buettner 001 (KDD, 2024)

Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention. - Alexander Koebler; Thomas Decker 004; Ingo Thon; Volker Tresp; Florian Buettner 001 (AISTATS, 2025)

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations. - Thomas Decker 004; Volker Tresp; Florian Buettner 001 (CoRR, 2025)