Algorithmic decision-making plays a crucial role in shaping digital experiences, yet biases in these systems can amplify existing inequalities. In the music domain, recommender systems influence what artists gain exposure. This talk focuses on the music domain and examines fairness from the perspective of artists, with a particular focus on gender bias. In this talk, I will present research findings on how bias manifests in music recommender algorithms, with a particular focus on gender bias from the perspective of artists. I will discuss mitigation strategies aimed at fostering fairer exposure and highlights the challenges and opportunities of designing more equitable recommender systems.