Dating through the Filters

Abstract

In this essay, I explore ethical considerations that might arise from the use of collaborative filtering algorithms on dating apps. Collaborative filtering algorithms can predict the preferences of a target user by looking at the past behavior of similar users. By recommending products through this process, they can influence the news we read, the movies we watch, and more. They are extremely powerful and effective on platforms like Amazon and Google. Recommender systems on dating apps are likely to group people by race, since they exhibit similar patterns of behavior: users on dating platforms seem to segregate themselves based on race, exclude certain races from romantic and sexual consideration (except their own), and generally show a preference for white men and women. As collaborative filtering algorithms learn from these patterns to predict preferences and build recommendations, they can homogenize the behavior of dating app users and exacerbate biased sexual and romantic behavior.