Date of Award


Document Type


Degree Name



Computer Science and Engineering

First Advisor

Sanchari Das

Second Advisor

Maria Calbi

Third Advisor

Daniel Pittman

Fourth Advisor

Kerstin Haring


Bias mitigation methods, Gender bias, Literature review, Machine learning, Music recommender system (MRS), User study


The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This thesis takes a closer look into gender bias discovered in different ML/AI applications and provides a holistic view of bias mitigation measures proposed in literature. Then by means of user study on 20 participants this paper analyzes gender bias in music recommender systems and the efficiency of bias mitigation methods. Instead of detailing the bias mitigation methods in technical terms, this paper takes the approach of utilizing user reviews to understand the effectiveness of bias mitigation methods for gender biases. Finally, this work aims to propose solutions that can help create equitable ML/AI systems that profits all stakeholders.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.


Received from ProQuest

Rights holder

Sunny Shrestha

File size

173 pgs

File format





Computer science, Artificial intelligence, Music

Available for download on Thursday, April 11, 2024