Music recommendation algorithms are unfair to women artists — but we can fix that
Better representation of women and gender minorities isn’t impossible
Our study
While previous studies have repeatedly asked consumers for their opinion, the music artists, those providing the content, arerarely in the loop.
We wanted to put the spotlight on artists. We asked musicians to give us their views on what would make online music platforms more fair. When they said gender imbalance was a major problem, we decided to study this in more detail.
Our analysis of around 330,000 users’ listening behavior over nine years showed a clear picture – only 25% of the artists ever listened to were female. When we tested the algorithm we found, on average, the first recommended track was by a man, along with the next six. Users had to wait until song seven or eight to hear one by a woman.
Breaking the loop
As users listen to the recommended songs, the algorithm learns from these. This creates a feedback loop.
To break this feedback loop, we came up with a simple approach to gradually give more exposure to female artists. We took the recommendations computed by the basic algorithm and re-ranked them – moving male artists a specified number of positions downwards.
In a simulation, we studied how our re-ranked recommendations could affect users’ listening behavior in the longer term. With the help of our re-ranked algorithm, users would start changing their behavior. They would listen to more female artists than before.
Eventually, the recommender started to learn from this change in behavior. It began to place females higher up in the recommended list, even before our re-ranking. In other words, we broke the feedback loop.
This shows how easy it can be. Our simple method can help address the biases in the algorithms that play a large role in the way many people discover new music and artists. Next, we hope to study how real consumers perceive the changes introduced by the re-ranking strategy and how it impacts their listening behavior in the long term.
Another crucial step would be to collect and use data about the wide scale of gender identities. We’re aware this binary gender classification does not reflect themultitude of gender identities. The unavailability of data beyond the gender binary is a massive obstacle, both for research as well as for taking action and making progress on a societal level.
So far, our simulation could demonstrate the benefits of a simple re-ranking approach. But responsibility is, of course, not with the platform providers alone. Initiatives such asKeychangeandWomen in Musicare working to represent the underrepresented in the music industry. The rest of us need to follow.
As music festivals are being criticized for thelack of womenin their lineups, any step towards representing more women all genders in a more balanced manner is a step in the right direction.
This article byChristine Bauer, Assistant Professor of Human Centred Computing,Utrecht UniversityandAndrés Ferraro, PhD Candidate, Information and Communication Technologies,Universitat Pompeu Fabra, is republished fromThe Conversationunder a Creative Commons license. Read theoriginal article.
Story byThe Conversation
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