Technical Reports - Query Results

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OFAI-TR-2018-03 ( 650kB PDF file)

Technical algorithmic bias in a music recommender

Arthur Flexer, Monika Dörfler, Jan Schlüter, Thomas Grill

Our work brings the problem of technical algorithm bias to the attention of the music information retrieval (MIR) community. We illustrate this so far neglected problem for a real world music recommender, where due to a problem of measuring distances in high dimensional spaces, songs closer to the center of all data are recommended over and over again, while songs far from the center are not recommended at all. We show that these so-called hub songs do not carry a specific semantic meaning and that deleting them from the data base promotes other songs to hub songs being recommended disturbingly often as a consequence. We argue for the ethical responsibility of MIR researchers to assure that their algorithms are unbiased and fair.

Keywords: Algorithmic bias, Music recommendation, Ethical aspects

Citation: Flexer A., Dörfler M., Schlüter J., Grill T.: Technical algorithmic bias in a music recommender (extended abstract), Late Breaking / Demos Session, 19th International Society for Music Information Retrieval Conference, 2018.