General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (xxAI@ICML, 2020)

Pitfalls to Avoid when Interpreting Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (CoRR, 2020)

Counterfactual Explanations & Adversarial Examples - Common Grounds, Essential Differences, and Potential Transfers. - Timo Freiesleben (CoRR, 2020)

Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). - Gunnar König; Timo Freiesleben; Bernd Bischl; Giuseppe Casalicchio; Moritz Grosse-Wentrup (CoRR, 2021)

A Causal Perspective on Meaningful and Robust Algorithmic Recourse. - Gunnar König; Timo Freiesleben; Moritz Grosse-Wentrup (CoRR, 2021)

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. - Christoph Molnar; Timo Freiesleben; Gunnar König; Giuseppe Casalicchio; Marvin N. Wright; Bernd Bischl (CoRR, 2021)

The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples. - Timo Freiesleben (Minds Mach., 2022)

Improvement-Focused Causal Recourse (ICR). - Gunnar König; Timo Freiesleben; Moritz Grosse-Wentrup (AAAI, 2023)

Dear XAI Community, We Need to Talk! - Fundamental Misconceptions in Current XAI Research. - Timo Freiesleben; Gunnar König (xAI (1), 2023)

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. - Christoph Molnar; Timo Freiesleben; Gunnar König; Julia Herbinger; Tim Reisinger; Giuseppe Casalicchio; Marvin N. Wright; Bernd Bischl (xAI (1), 2023)

Artificial Neural Nets and the Representation of Human Concepts. - Timo Freiesleben (CoRR, 2023)

What does explainable AI explain? - Timo Freiesleben (2023)

CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. - Susanne Dandl; Kristin Blesch; Timo Freiesleben; Gunnar König; Jan Kapar; Bernd Bischl; Marvin N. Wright (xAI (3), 2024)

Foundation models in healthcare require rethinking reliability. - Thomas Grote; Timo Freiesleben; Philipp Berens (Nat. Mac. Intell., 2024)

Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena. - Timo Freiesleben; Gunnar König; Christoph Molnar; Álvaro Tejero-Cantero (Minds Mach., 2024)

Performative Validity of Recourse Explanations. - Gunnar König; Hidde Fokkema; Timo Freiesleben; Celestine Mendler-Dünner; Ulrike von Luxburg (CoRR, 2025)