Exploring Artist Gender Bias in Music Recommendation

By Dougal Shakespeare, Lorenzo Porcaro, Emilia G'omez, Carlos Castillo. 2020

46 citations recorded by the source corpus.

Music Recommender Systems (mRS) are designed to give personalised and meaningful 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 perspectives, including their potential for discrimination, is still a novel evaluation practice in the music domain. In this work, we center 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 deployed 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 preferences, highlighting the effect such configurations can have on user's gender bias after recommendation generation.

Subfields: Information Systems, Safety, Robustness & Interpretability, Signal Processing

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Citations in the corpus

Towards Understanding and Mitigating Social Biases in Language Models (2021, background): “, 2020), recommendation systems (Shakespeare et al., 2020), and search engines (Baeza-Yates, 2016; Otterbacher et al.”