Social Group Bias in AI Finance

TL;DR

This paper examines racial bias in large language models used for credit decisions, introducing a framework to detect and mitigate bias, achieving significant reduction without harming model performance.

Contribution

It presents a reproducible counterfactual testing framework and a control-vector intervention to identify and reduce racial bias in financial LLMs.

Findings

Significant race-based discrepancies exceeding historical bias levels.

Control-vector intervention reduces racial disparities by up to 70%.

Bias mitigation does not impair overall model performance.

Abstract

Financial institutions increasingly rely on large language models (LLMs) for high-stakes decision-making. However, these models risk perpetuating harmful biases if deployed without careful oversight. This paper investigates racial bias in LLMs specifically through the lens of credit decision-making tasks, operating on the premise that biases identified here are indicative of broader concerns across financial applications. We introduce a reproducible, counterfactual testing framework that evaluates how models respond to simulated mortgage applicants identical in all attributes except race. Our results reveal significant race-based discrepancies, exceeding historically observed bias levels. Leveraging layer-wise analysis, we track the propagation of sensitive attributes through internal model representations. Building on this, we deploy a control-vector intervention that effectively reduces racial disparities by up to 70% (33% on average) without impairing overall model performance. Our approach provides a transparent and practical toolkit for the identification and mitigation of bias in financial LLM deployments.