Music Recommender Systems (mRS) are designed to give personalised and meaning-ful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music preferences. Whilst accuracy metrics have been widely applied to evaluate recommendations in mRS literature, evaluating a user's item utility from other impact-oriented perspec-tives, including their potential for discrimination, is still a novel evaluation

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... ce in the music domain. In this work, we centre our attention on a specific phenomenon for which we want to estimate if mRS may exacerbate its impact: gender bias. Our work presents an exploratory study, analyzing the extent to which commonly de-ployed state of the art Collaborative Filtering (CF) algorithms may act to further increase or decrease artist gender bias. To assess group biases introduced by CF, we deploy a recently proposed metric of bias disparity on two listening event datasets: the LFM-1b dataset, and the earlier constructed Celma's dataset. Our work traces the causes of disparity to variations in input gender distributions and user-item pref-erences, highlighting the e˙ect such configurations can have on user's gender bias after recommendation generation.