Work

How fair is machine learning in credit lending?

Year: 2024

Type: article

Abstract: Machine learning models are widely used to decide whether to accept or reject credit loan applications. However, similarly to human‐based decisions, they may discriminate between special groups of applicants, for instance based on age, gender, and race. In this paper, we aim to understand whether machine learning credit lending models are biased in a real case study, that concerns borrowers asking for credits in different regions of the United States. We show how to measure model fairness using different metrics, and we explore the capability of explainable machine learning to add further insights. From a constructive viewpoint, we propose a propensity matching approach that can improve fairness.

Source: Quality and Reliability Engineering International

DOI: 10.1002/qre.3579

Authors: Golnoosh Babaei, Paolo Giudici

Institution: University of Pavia

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $4,330