2 papers
cs.LG2026
How Complexity Contributes to Learning Opacity in Machine Learning
Joachim Stein, Eric Raidl
Machine learning (ML) algorithms are known to be opaque. We do not know the reasons for their predictions. The learning process leading to the prediction function is also opaque. W…
cs.LG2024
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…