End-user Algorithmic Auditing for Music Discoverability: A Research Roadmap

The evolution of online platforms over the past decades has radically transformed the way people discover music, and thanks to social media and music streaming services nowadays listeners have access to an ever-increasing amount of tracks and artists. Within these platforms, one of the goals of recommender systems is to help users discover music without making them feel overwhelmed while exploring the huge catalogues available. However, these systems have come under scrutiny from the scientific community, policy-makers, and civil society due to their potential negative societal impact, notably with regard to issues of fairness, non-discrimination, inclusion and diversity. Algorithmic auditing has emerged as a tool to analyse the problematic behaviours exhibited by recommenders, and to offer remedies that can limit their negative impact. In this position paper, we advocate for the involvement of end-users in the auditing process, which can contribute to the recognition, analysis, and mitigation of problematic behaviours which may arise while discovering music. Highlighting how recommenders, by influencing the discoverability of music, may impact listeners’ exposure to culturally diverse content, we seek to address the challenges posed by music recommender systems problematic behaviours, ultimately with the goal of fostering a more inclusive and diverse environment for music discovery within the digital landscape.