Fairness of Classification Using Users? Social Relationships in Online Peer-To-Peer Lending

Yanying Li, Yue Ning, Rong Liu, Ying Wu, Wendy Hui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Peer-to-peer (P2P) lending marketplaces on the Web have been growing over the last decade. By providing online platforms, P2P lending enables individuals to borrow and lend money directly from and to one another. Since the applicants on P2P lending platforms may lack sufficient financial history for assessment, quite a few P2P lending service providers have been utilizing the applicants' social relationships to improve the risk prediction accuracy of loan applications. However, utilizing the information of applicants' social relationships may introduce discrimination in prediction. In this paper, we analyze and evaluate the impact of the applicants' social relationships on the fairness of risk prediction for P2P lending. We investigate over a million loan records collected from Prosper.com, one of the leading P2P lending companies in the world. We construct the Prosper social network of loan borrowers and lenders, and generate the social features of applicants by adapting a state-of-the-art social credit scoring scheme to the Prosper social network. We consider two types of fairness notions in the literature, namely individual fairness and counterfactual fairness. Our results demonstrate that the social score harms both individual and counterfactual fairness of classification. To address this issue, we design two new algorithms that mitigate bias by generalizing social features. Our experimental results show that our mitigation algorithms can reduce bias while utilizing social scores effectively.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
Pages733-742
Number of pages10
ISBN (Electronic)9781450370240
DOIs
StatePublished - 20 Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period20/04/2024/04/20

Keywords

  • Algorithmic fairness
  • machine learning
  • social network

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