Bias Disparity in Recommendation Systems

TL;DR

This paper investigates how recommendation systems can amplify biases present in user data, explores conditions leading to bias disparity through experiments, and evaluates a re-ranking method to mitigate this issue.

Contribution

It provides a preliminary experimental analysis of bias disparity in recommender systems and proposes a simple re-ranking approach to reduce bias amplification.

Findings

Bias disparity occurs under certain data and algorithmic conditions.

Re-ranking can help mitigate bias disparity.

Long-term recommendations influence data bias evolution.

Abstract

Recommender systems have been applied successfully in a number of different domains, such as, entertainment, commerce, and employment. Their success lies in their ability to exploit the collective behavior of users in order to deliver highly targeted, personalized recommendations. Given that recommenders learn from user preferences, they incorporate different biases that users exhibit in the input data. More importantly, there are cases where recommenders may amplify such biases, leading to the phenomenon of bias disparity. In this short paper, we present a preliminary experimental study on synthetic data, where we investigate different conditions under which a recommender exhibits bias disparity, and the long-term effect of recommendations on data bias. We also consider a simple re-ranking algorithm for reducing bias disparity, and present some observations for data disparity on real data.