182 citations · 202 across the 9 of their papers we have counts for
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Ludwig Bothmann, Philip A. Boustani, Jose M. Alvarez +3
Bias-transforming methods of fairness-aware machine learning aim to correct a non-neutral status quo with respect to a protected attribute (PA). Current methods, however, lack an e…
mlr3summary: Concise and interpretable summaries for machine learning models
Susanne Dandl, Marc Becker, Bernd Bischl +2
This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models…
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
Ludwig Bothmann, Susanne Dandl, Michael Schomaker
A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning (ML) models that…