Gender Bias in Spotify Recommendations
Conference Year
2024
Abstract
As machine learning becomes more and more prevalent, so does the bias it breeds and perpetuates. Recommendation algorithms, in particular, govern much of our interactions with technology and content online, leading to significant consequences to various stakeholders if unjust. This project analyzes various types of gender bias in Spotify’s Discover Weekly recommendation algorithm by analyzing recommended output for experimental users of different genders and preferred genres. Ultimately, this experiment serves as an example of the kind of work required to assess recommender systems for bias and suggests a framework for doing so by considering stakeholders and their competing fairness objectives. Applying this framework on Spotify, the simulation suggests consistent bias on the basis of the producer’s gender acting on individual women and groups of women in Spotify recommendations as well as bias on the basis of the user’s gender. The effects of this bias differ by genre in users’ taste profiles.
Primary Faculty Mentor Name
Lisa Dion
Graduate Student Mentors
Jason Hibbeler
Status
Undergraduate
Student College
College of Arts and Sciences
Second Student College
College of Engineering and Mathematical Sciences
Program/Major
Data Science
Second Program/Major
Computer Science
Primary Research Category
Engineering and Math Science
Gender Bias in Spotify Recommendations
As machine learning becomes more and more prevalent, so does the bias it breeds and perpetuates. Recommendation algorithms, in particular, govern much of our interactions with technology and content online, leading to significant consequences to various stakeholders if unjust. This project analyzes various types of gender bias in Spotify’s Discover Weekly recommendation algorithm by analyzing recommended output for experimental users of different genders and preferred genres. Ultimately, this experiment serves as an example of the kind of work required to assess recommender systems for bias and suggests a framework for doing so by considering stakeholders and their competing fairness objectives. Applying this framework on Spotify, the simulation suggests consistent bias on the basis of the producer’s gender acting on individual women and groups of women in Spotify recommendations as well as bias on the basis of the user’s gender. The effects of this bias differ by genre in users’ taste profiles.