38 citations · 50 across the 10 of their papers we have counts for
4 papers · 1 filter
Are All Genders Equal in the Eyes of Algorithms? -- Analysing Search and Retrieval Algorithms for Algorithmic Gender Fairness
Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher +3
Algorithmic systems such as search engines and information retrieval platforms significantly influence academic visibility and the dissemination of knowledge. Despite assumptions o…
Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
The correct way to quantify predictive uncertainty in neural networks remains a topic of active discussion. In particular, it is unclear whether the state-of-the art entropy decomp…
Privilege Scores
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…
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…