1 code implementation • 12 Jan 2024 • Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina MC Höhne
The Model Parameter Randomisation Test (MPRT) is widely acknowledged in the eXplainable Artificial Intelligence (XAI) community for its well-motivated evaluative principle: that the explanation function should be sensitive to changes in the parameters of the model function.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 13 Dec 2023 • Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M. -C. Höhne
Grasslands are known for their high biodiversity and ability to provide multiple ecosystem services.
1 code implementation • 1 Mar 2023 • Philine Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, Marina M. -C. Höhne
We find architecture-dependent performance differences regarding robustness, complexity and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 14 Feb 2023 • Anna Hedström, Philine Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M. -C. Höhne
We address this problem through a meta-evaluation of different quality estimators in XAI, which we define as ''the process of evaluating the evaluation method''.
1 code implementation • NeurIPS 2023 • Anna Hedström, Leander Weber, Dilyara Bareeva, Daniel Krakowczyk, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M. -C. Höhne
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness.
2 code implementations • 18 Jun 2021 • Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M. -C. Höhne
For local explanation, stochasticity is known to help: a simple method, called SmoothGrad, has improved the visual quality of gradient-based attribution by adding noise to the input space and averaging the explanations of the noisy inputs.