Gender Bias

Fairness in algorithmic decision-making: The effects of bias mitigation strategies in music recommender systems
Invited talk for the “Forschungsseminar BWL” at the Department of Business at Paris Lodron University Salzburg.
Christine Bauer
20 November 2024
Project
Breaking feedback loops in recommender systems: an example on mitigating artist gender bias in the music domain
Invited talk at the University of Regensburg.
18 December 2023
Project
Breaking feedback loops in recommender systems: an example on mitigating artist gender bias in the music domain
Music recommendation systems from the perspective of artists: what constitutes fairness?
Invited talk at JADS.
28 November 2023
Project
Music recommendation systems from the perspective of artists: what constitutes fairness?
Strategies for mitigating artist gender bias in music recommendation: a simulation study
Recommender systems are prone to various biases. Hence, bias mitigation approaches are needed to counteract those. In the music sector, …
Christine Bauer, Andrés Ferraro
PDF Cite Project DOI
Fairness—Are algorithms a burden or a solution?
Invited talk at the MediaFutures Seminar at the University of Bergen.
21 April 2022
Project Slides Video
Fairness—Are algorithms a burden or a solution?
What is fair? The artists' perspective on music recommenders
Invited talk at the Music and Audio Research lab—a research school within New York University.
6 April 2022
Project
What is fair? The artists' perspective on music recommenders
Keynote: Celebrating an engaged music community and breaking the bias
Keynote at Codarts Rotterdam—International Women’s Day 2022.
8 March 2022
Project Program
Keynote: Celebrating an engaged music community and breaking the bias
Geschlechtsspezifische Vorurteile und Diskriminierung durch KI
Panelist at VUT Indie Days Köln 2020.
Christine Bauer, Stephan Baumann, Amélie Heldt, Ronny Krieger
23 October 2020
Video
Geschlechtsspezifische Vorurteile und Diskriminierung durch KI