38 citations · 50 across the 10 of their papers we have counts for
4 papers · 1 filter
Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective
Charlotte Leininger, Simon Rittel, Ludwig Bothmann
Training machine learning models for fair decisions faces two key challenges: The \emph{fairness-accuracy trade-off} results from enforcing fairness which weakens its predictive pe…
A Guide to Feature Importance Methods for Scientific Inference
Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright +3
While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding…
Evaluating machine learning models in non-standard settings: An overview and new findings
Roman Hornung, Malte Nalenz, Lennart Schneider +5
Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, parti…
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations
Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl +1
This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation's feature values c…