4 citations · 9 across the 7 of their papers we have counts for
7 papers
A Unifying Perspective on Probabilities as Model Predictions
Benedikt Höltgen
Although probabilistic statements are ubiquitous, foundational disagreements persist about their understanding, as exemplified by debates between Bayesians and frequentists; moreov…
Gender Artifacts from Art History to Text-to-Image Generation
Piera Riccio, Miriam Doh, Benedikt Höltgen +2
Artistic styles are rooted in specific socio-historical contexts that encode social hierarchies, including distinct constructions of gender. Yet in AI research, style has long been…
Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity
Benedikt Höltgen, Nuria Oliver
Fairness through Unawareness (FtU) describes the idea that discrimination against demographic groups can be avoided by not considering group membership in the decisions or predicti…
The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social World
Benedikt Höltgen, Robert C. Williamson
Machine Learning research, including work promoting fair or equitable algorithms, often relies on the concept of a data-generating probability distribution. The standard presumptio…
On the Richness of Calibration
Benedikt Höltgen, Robert C Williamson
Probabilistic predictions can be evaluated through comparisons with observed label frequencies, that is, through the lens of calibration. Recent scholarship on algorithmic fairness…
DeDUCE: Generating Counterfactual Explanations Efficiently
Benedikt Höltgen, Lisa Schut, Jan M. Brauner +1
When an image classifier outputs a wrong class label, it can be helpful to see what changes in the image would lead to a correct classification. This is the aim of algorithms gener…
Encoding Causal Macrovariables
Benedikt Höltgen
In many scientific disciplines, coarse-grained causal models are used to explain and predict the dynamics of more fine-grained systems. Naturally, such models require appropriate m…