Timo Freiesleben

According to our database, Timo Freiesleben authored at least 19 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Explainable AI Isn't Enough! Rethinking Algorithmic Contestability.

Timo Freiesleben, Kristof Meding, Gunnar König

CoRR, May, 2026

Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks.

Timo Freiesleben

CoRR, March, 2026

2025

The Benchmarking Epistemology: Construct Validity for Evaluating Machine Learning Models.

Timo Freiesleben, Sebastian Zezulka

CoRR, October, 2025

Performative Validity of Recourse Explanations.

Gunnar König, Hidde Fokkema, Timo Freiesleben, Celestine Mendler-Dünner, Ulrike von Luxburg

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

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., September, 2024

Foundation models in healthcare require rethinking reliability.

Thomas Grote, Timo Freiesleben, Philipp Berens

Nat. Mac. Intell., 2024

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

Proceedings of the Explainable Artificial Intelligence, 2024

2023

What does explainable AI explain?

Timo Freiesleben

PhD thesis, 2023

Artificial Neural Nets and the Representation of Human Concepts.

Timo Freiesleben

CoRR, 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

Proceedings of the Explainable Artificial Intelligence, 2023

Dear XAI Community, We Need to Talk! - Fundamental Misconceptions in Current XAI Research.

Timo Freiesleben, Gunnar König

Proceedings of the Explainable Artificial Intelligence, 2023

Improvement-Focused Causal Recourse (ICR).

Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.

Timo Freiesleben

Minds Mach., 2022

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

A Causal Perspective on Meaningful and Robust Algorithmic Recourse.

Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup

CoRR, 2021

Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT).

Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup

CoRR, 2021

2020

Counterfactual Explanations & Adversarial Examples - Common Grounds, Essential Differences, and Potential Transfers.

Timo Freiesleben

CoRR, 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

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

Proceedings of the xxAI - Beyond Explainable AI, 2020