Inclusive Decision Making via Contrastive Learning and Domain Adaptation

50 Pages Posted: 5 Jul 2023

See all articles by Xiyang Hu

Xiyang Hu

Carnegie Mellon University

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business

Beibei Li

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Tian Lu

Department of Information Systems, Arizona State University

Date Written: July 31, 2024

Abstract

Inclusive decision-making is crucial for promoting social justice and welfare, particularly in high-stakes scenarios like loan screening. We propose using contrastive learning and domain adaptation to improve inclusion in algorithmic decision-making. Specifically, we focus on micro-lending, which has played a significant role in increasing access to financial services. Traditional machine learning algorithms for credit evaluation face the selective labels problem, as the training data usually only contains default outcome labels from approved loan applications that tend to represent borrowers with more favorable socioeconomic characteristics. Consequently, these algorithms struggle to effectively generalize to disadvantaged borrowers. To overcome this problem, we introduce a Transformer-based loan screening model that leverages self-supervised contrastive learning and domain adaptation. Our model uses contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and employs domain adaptation to generalize the performance of our label predictor. We evaluate our approach on a real-world micro-lending dataset and demonstrate its effectiveness. The results show that our approach significantly enhances inclusion in funding decisions, while simultaneously improving loan screening accuracy and lender profit by 7.10% and 8.95%, respectively. Additionally, we find that incorporating test data and labeling a small ratio of it further enhances model performance.

Keywords: Contrastive Learning, Domain Adaptation, FinTech, Inclusion, Representation Bias

Suggested Citation

Hu, Xiyang and Huang, Yan and Li, Beibei and Lu, Tian, Inclusive Decision Making via Contrastive Learning and Domain Adaptation (July 31, 2024). Available at SSRN: https://ssrn.com/abstract=4496106 or http://dx.doi.org/10.2139/ssrn.4496106

Xiyang Hu

Carnegie Mellon University ( email )

Pittsburgh, PA
United States

HOME PAGE: http://www.andrew.cmu.edu/user/xiyanghu/

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Beibei Li

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

Tian Lu (Contact Author)

Department of Information Systems, Arizona State University ( email )

Tempe, AZ 85287
United States

HOME PAGE: http://isearch.asu.edu/profile/tianlu1

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