Recommender systems play an important role in everyday life. These systems assist users in choosing products to buy, movies to watch, or news articles to read. With their wide usage, there is an increasing pressure that such systems are fair. Besides serving diverse groups of users, recommenders need to represent and serve item providers in a fair manner, too. But what is fair? In this talk, I will present research on fairness in music recommender systems taking the artists’ perspective. What do artists consider fair? Are algorithms a burden or a solution? In particular, I will zoom in on recent research on gender bias in music recommenders and how we can address this issue.