Work

A Systematic Approach to Auditing and Mitigating Demographic Bias in Algorithmic Lending Models

Year: 2026

Type: preprint

Abstract: Algorithmic credit-scoring and lending models, increasingly used to automate loan approval decisions, risk encoding and amplifying historical demographic biases present in training data, potentially resulting in systematically unfair outcomes for protected groups. This project develops a systematic auditing framework to detect demographic bias in algorithmic lending models across multiple fairness metrics, coupled with mitigation techniques applied at the pre-processing, in-processing, and post-processing stages, to produce lending models that are both predictive and demonstrably more equitable.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22022838

Author: GUPTA, ADISHREE

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W7203928415

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