3 papers
cs.LG2026
We Need Explanation Cards to Connect Explanation Algorithms to the Real World
Eric Günther, Balázs Szabados, Kristof Meding +3
Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short. First, the meaning of algorithmic explan…
cs.LG2025
Informative Post-Hoc Explanations Only Exist for Simple Functions
Eric Günther, Balázs Szabados, Robi Bhattacharjee +2
Many researchers have suggested that local post-hoc explanation algorithms can be used to gain insights into the behavior of complex machine learning models. However, theoretical g…
cs.LG2024
Disentangling Interactions and Dependencies in Feature Attribution
Gunnar König, Eric Günther, Ulrike von Luxburg
In explainable machine learning, global feature importance methods try to determine how much each individual feature contributes to predicting the target variable, resulting in one…