54 citations · 117 across the 18 of their papers we have counts for
4 papers · 1 filter
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD
Lea Demelius, Dominik Kowald, Simone Kopeinik +2
Differential privacy (DP) is a prominent method for protecting information about individuals during data analysis. Training neural networks with differentially private stochastic g…
Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?
Peter Muellner, Anna Schreuer, Simone Kopeinik +2
Algorithmic decision-support systems, i.e., recommender systems, are popular digital tools that help tourists decide which places and attractions to explore. However, algorithms of…
Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language
Kristin Gnadt, David Thulke, Simone Kopeinik +1
In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods…
Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
Andrea Forster, Simone Kopeinik, Denic Helic +2
Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias, which disadvantages less popula…