Zoom meeting
Music information retrieval (MIR) has become an indispensable asset in the music industry. It powers music recommendations for listeners and supports artists in mastering their crafts. While MIR has made remarkable progress, we need to improve in serving the multifaceted needs of stakeholders who rely on these technologies. Taking examples from music recommender systems, I will demonstrate the potential risks of neglecting artists’ needs and provide strategies for mitigation.

Christine Bauer is EXDIGIT Professor of Interactive Intelligent Systems at the Department of Artificial Intelligence and Human Interfaces (AIHI) at the Paris Lodron University Salzburg, Austria. Her research centers on interactive intelligent systems, where she integrates research on intelligent technologies, the interaction of humans with an intelligent system, and their interplay. She takes a human-centered perspective, where technology follows humans’ and society’s needs. In recent years, she worked on context-aware recommender systems in the music and media domains. The core interests in her research activities are fairness and multi-method evaluations. She has authored more than 100 papers and holds several best paper awards and many awards for her reviewing activities. She received the prestigious Elise Richter career research grant (2017–2020), funded by the Austrian Science Fund (FWF). She is on the Editorial Board of ACM Transactions on Recommender Systems (TORS) and co-organizes the Workshop series “Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES)”. She advocates for equal opportunities and engages in initiatives such as Women in Music Information Retrieval (WiMIR) and the Allyship program at CHI.