Fairness in algorithmic decision-making is a critical concern across various domains. In this talk, I focus on the music domain, where recommender systems have become indispensable, helping users navigate vast catalogs by suggesting similar artists or the next track to play. While these systems’ goal is to recommend the ‘right music to the right person at the right moment’, they often fall short of this ideal, raising questions about fairness and bias. In this talk, I focus on fairness from the perspective of artists, addressing how biases—such as gender bias—manifest in music recommendations and affect artist exposure. I will present research findings on gender bias and explore strategies for their mitigation.