Technology and music have a centuries old history of coexistence: from luthiers to music information research. The emergence of machine learning for artificial intelligence in music technology has the potential to change the way music is experienced, learned, played and listened. This raises concerns related to its fair and transparent use, avoiding discrimination, designing sustainable experimental frameworks, and being aware of the biases the algorithms and datasets have. The first edition of the Workshop Designing Human-Centric Music Information Research systems aims at bringing together people interested in discussing the ethical implications of our technologies and proposing robust ways to assess our system for discrimination, sustainability, and transparency. We strongly believe that research on fairness, accountability, transparency advances through multi-disciplinary research. Thus, this first edition hosts two keynotes talks which bring a refreshing perspective from two different fields, economics and human-computer interaction. First, Luis Aguiar, University of Zurich, presents ”Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists”. Second, Nava Tintarev, Delft University of Technology, presents ”Supporting User Control for Music Recommendations”. We would like to thank our keynote speakers and the participants for their insightful presentations and for contributing to the discussion. Finally, we would like to thank Jaehun Kim and Ginny Ruiter who assisted us in organizing the venue.