9 papers
Robustness and User-Perceived Value of Popularity Calibration in Music Recommendation: A User Study
Oleg Lesota, Gustavo Escobedo, Bruce Ferwerda +4
Popularity calibration in recommender systems has been studied both as a form of user-centered personalization and as an indicator of popularity bias. Most existing work evaluates…
From Skill Extraction to Multistakeholder Recommendation: A Two-Stage Framework for Bias Governance in Skills-Based Job Matching
Andrea Forster, Gregor Autischer, Dominik Kowald +1
AI-based labor-market systems or platforms can affect access to job opportunities prior to organizational candidate rankings or hiring decisions. Such applications warrant caution,…
Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs
Richard Hohensinner, Belgin Mutlu, Inti Gabriel Mendoza Estrada +3
Large language models (LLMs) are deployed at scale, yet their training data life cycle remains opaque. This survey synthesizes research from the past ten years on three tightly cou…
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…