Recommender systems are central to contemporary music listening, yet their problematic behaviors remain underexplored from the perspective of everyday listeners. While prior research has addressed issues such as bias and diversity, less is known about how users themselves perceive and interpret these dynamics in relation to music discoverability. This paper reports on think-aloud interviews with 20 Italian digital-native listeners, who completed discovery-oriented tasks while reflecting on algorithmic recommendations. Thematic analysis revealed three recurring concerns: reinforcement of societal biases, commercial imperatives driving exposure, and confinement within narrow niches. These findings show how listeners actively develop folk theories of recommender behavior, highlighting a tension between algorithmic efficiency and cultural effects. We contribute empirical insights into user sensemaking of algorithmic harms, consolidate the use of the Think-Aloud Protocol as a user-driven auditing method, and outline design implications for more participatory and equitable music recommender systems.
ACM CHI Conference on Human Factors in Computing Systems