Personalized fairness-aware re-ranking for microlending

Abstract

Microlending can lead to improved access to capital in impoverished countries. Recommender systems could be used in microlending to provide efficient and personalized service to lenders. However, increasing concerns about discrimination in machine learning hinder the application of recommender systems to the microfinance industry. Most previous recommender systems focus on pure personalization, with fairness issue largely ignored. A desirable fairness property in microlending is to give borrowers from different demographic groups a fair chance of being recommended, as stated by Kiva. To achieve this goal, we propose a Fairness-Aware Re-ranking (FAR) algorithm to balance ranking quality and borrower-side fairness. Furthermore, we take into consideration that lenders may differ in their receptivity to the diversification of recommended loans, and develop a Personalized Fairness-Aware Re-ranking (PFAR) algorithm. Experiments on a real-world dataset from Kiva.org show that our re-ranking algorithm can significantly promote fairness with little sacrifice in accuracy, and be attentive to individual lender preference on loan diversity.