Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings. - Lukas Klein; João B. S. Carvalho; Mennatallah El-Assady; Paolo Penna; Joachim M. Buhmann; Paul F. Jaeger (MIDL, 2022)

From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process. - Lukas Klein; Mennatallah El-Assady; Paul F. Jäger (CoRR, 2022)

A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification. - Paul F. Jaeger; Carsten T. Lüth; Lukas Klein; Till J. Bungert (ICLR, 2023)

Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment. - Carsten T. Lüth; Till J. Bungert; Lukas Klein; Paul F. Jaeger (NeurIPS, 2023)

Toward Realistic Evaluation of Deep Active Learning Algorithms in Image Classification. - Carsten T. Lüth; Till J. Bungert; Lukas Klein; Paul F. Jaeger (CoRR, 2023)

Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics. - Lukas Klein; Carsten T. Lüth; Udo Schlegel; Till J. Bungert; Mennatallah El-Assady; Paul F. Jaeger (NeurIPS, 2024)

Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities. - Kenza Amara; Lukas Klein; Carsten T. Lüth; Paul Jäger 002; Hendrik Strobelt; Mennatallah El-Assady (CoRR, 2024)

Enhancing Predictive Imaging Biomarker Discovery Through Treatment Effect Analysis. - Shuhan Xiao; Lukas Klein; Jens Petersen; Philipp Vollmuth; Paul F. Jaeger; Klaus H. Maier-Hein (WACV, 2025)