Behind the Screen: Gender Bias as an “Male/Algorithmic Gaze” in Netflix’s Recommendation System

Abstract

This thesis examines the impact of Netflix's recommendation algorithms on gender representation, with a particular focus on the possibility that these algorithms may serve to reinforce existing gender biases through the content they recommend. Such biases may extend from the real world into the digital world. The study examines the concept of the "male gaze" and considers the portrayal of female-oriented content in algorithmic recommendation systems as an example of an "algorithmic gaze." Furthermore, it examines how algorithm-driven recommendations reinforce male-centric narratives, thereby potentially limiting the diversity of content available to female audiences. By analysing Netflix's genre classifications, particularly through "Alt-Genres", and the gender distribution of protagonists, this research reveals disparities in the representation and promotion of male and female characters on the platform.