39 citations · 95 across the 35 of their papers we have counts for
10 papers · 1 filter
Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias
Aditya Parikh, Eike Petersen, Stella Frank +3
Encoded demographic information in internal model representations is a commonly assumed risk factor for algorithmic bias, with demographic representation invariance often being tou…
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content
Miguel López-Pérez, Marco Miani, Valery Naranjo +2
Generative models are popular for medical imaging tasks such as anomaly detection, feature extraction, data visualization, or image generation. Since they are parameterized by deep…
Interpreting Equivariant Representations
Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
Latent representations are used extensively for downstream tasks, such as visualization, interpolation or feature extraction of deep learning models. Invariant and equivariant neur…
Are demographically invariant models and representations in medical imaging fair?
Eike Petersen, Enzo Ferrante, Melanie Ganz +1
Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their pote…
On (assessing) the fairness of risk score models
Eike Petersen, Melanie Ganz, Sune Hannibal Holm +1
Recent work on algorithmic fairness has largely focused on the fairness of discrete decisions, or classifications. While such decisions are often based on risk score models, the fa…
Graph2Graph Learning with Conditional Autoregressive Models
Guan Wang, Francois Bernard Lauze, Aasa Feragen
We present a graph neural network model for solving graph-to-graph learning problems. Most deep learning on graphs considers ``simple'' problems such as graph classification or reg…