A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. - Maximilian V. Harl; Marvin Herchenbach; Sven Kruschel; Nico Hambauer; Patrick Zschech; Mathias Kraus (Wirtschaftsinformatik, 2022)

Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. - Sven Kruschel; Nico Hambauer; Sven Weinzierl; Sandra Zilker; Mathias Kraus; Patrick Zschech (CoRR, 2024)

Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability. - Sven Kruschel; Lasse Bohlen; Julian Rosenberger; Patrick Zschech; Mathias Kraus (CoRR, 2024)

Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users. - Julian Rosenberger; Philipp Schröppel; Sven Kruschel; Mathias Kraus; Patrick Zschech; Maximilian Förster (CoRR, 2025)

Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior? - Lasse Bohlen; Sven Kruschel; Julian Rosenberger; Patrick Zschech; Mathias Kraus (CoRR, 2025)