Scrutinizing XAI using linear ground-truth data with suppressor variables. - Rick Wilming; Céline Budding; Klaus-Robert Müller; Stefan Haufe (Mach. Learn., 2022)

Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables. - Rick Wilming; Leo Kieslich; Benedict Clark; Stefan Haufe (ICML, 2023)

Benchmark data to study the influence of pre-training on explanation performance in MR image classification. - Marta Oliveira; Rick Wilming; Benedict Clark; Céline Budding; Fabian Eitel; Kerstin Ritter; Stefan Haufe (CoRR, 2023)

XAI-TRIS: Non-linear benchmarks to quantify ML explanation performance. - Benedict Clark; Rick Wilming; Stefan Haufe (CoRR, 2023)

EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods. - Benedict Clark; Rick Wilming; Artur Dox; Paul Eschenbach; Sami Hached; Daniel Jin Wodke; Michias Taye Zewdie; Uladzislau Bruila; Marta Oliveira; Hjalmar Schulz; Luca Matteo Cornils; Danny Panknin; et al. (CoRR, 2024)

GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations. - Rick Wilming; Artur Dox; Hjalmar Schulz; Marta Oliveira; Benedict Clark; Stefan Haufe (CoRR, 2024)

Explainable AI needs formal notions of explanation correctness. - Stefan Haufe; Rick Wilming; Benedict Clark; Rustam Zhumagambetov; Danny Panknin; Ahcène Boubekki (CoRR, 2024)

Benchmarking the influence of pre-training on explanation performance in MR image classification. - Marta Oliveira; Rick Wilming; Benedict Clark; Céline Budding; Fabian Eitel; Kerstin Ritter; Stefan Haufe (Frontiers Artif. Intell., 2024)

XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance. - Benedict Clark; Rick Wilming; Stefan Haufe (Mach. Learn., 2024)