Bias Disparity in Recommendation Systems
It addresses bias amplification in recommendation systems, which can affect fairness in domains like employment and commerce, but is incremental as it builds on existing concerns with preliminary experiments.
The paper investigates bias disparity in recommender systems, where biases in user data can be amplified, and presents a preliminary study on synthetic data to explore conditions and long-term effects, along with a simple re-ranking algorithm for mitigation.
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.