Algorithms have seamlessly integrated into the music industry, with music recommendation systems facilitating navigation through vast catalogs of musical tracks. These systems suggest similar artists or recommend the next track for us to listen to. An ideal music recommendation system should recommend the ‘right music to the right person at the right moment.’ However, what happens when it falls short of being ideal? In this presentation, I delve into the perspective of artists, exploring their notions of fairness. Among others, I will present research findings on gender bias in music recommendations and provide strategies for mitigation.