Digital platforms increasingly rely on AI-driven recommender systems to enhance user experience. Music streaming platforms like Spotify, Apple Music, and Amazon Music compete on recommendation quality rather than content. However, as recommender systems mediate artist discovery, concerns about algorithmic fairness emerge, stemming from extensive studies on algorithmic bias. This study examines how artists navigate Spotify’s recommender system algorithms and perceive their impact on the competition among them for visibility. This paper draws on a traditional literature review on biases and fairness in recommender systems, with a particular focus on digital platforms, supplemented by preliminary interviews with artists. The study contributes to ongoing debates on digital platform fairness by extending the results to other music streaming platforms, offering implications for artists, industry stakeholders, and policymakers in the music industry.