16 citations · 25 across the 6 of their papers we have counts for
14 papers
Using predictive multiplicity to measure individual performance within the AI Act
Karolin Frohnapfel, Mara Seyfert, Sebastian Bordt +2
When building AI systems for decision support, one often encounters the phenomenon of predictive multiplicity: a single best model does not exist; instead, one can construct many m…
Auditing Local Explanations is Hard
Robi Bhattacharjee, Ulrike von Luxburg
In sensitive contexts, providers of machine learning algorithms are increasingly required to give explanations for their algorithms' decisions. However, explanation receivers might…
Rethinking Explainable Machine Learning as Applied Statistics
Sebastian Bordt, Eric Raidl, Ulrike von Luxburg
In the rapidly growing literature on explanation algorithms, it often remains unclear what precisely these algorithms are for and how they should be used. In this position paper, w…
Pitfalls of Climate Network Construction: A Statistical Perspective
Moritz Haas, Bedartha Goswami, Ulrike von Luxburg
Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight t…
Relating graph auto-encoders to linear models
Solveig Klepper, Ulrike von Luxburg
Graph auto-encoders are widely used to construct graph representations in Euclidean vector spaces. However, it has already been pointed out empirically that linear models on many t…
From Shapley Values to Generalized Additive Models and back
Sebastian Bordt, Ulrike von Luxburg
In explainable machine learning, local post-hoc explanation algorithms and inherently interpretable models are often seen as competing approaches. This work offers a partial reconc…