Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification

TL;DR

This paper introduces LDA-XGB1, a novel machine learning model that balances accuracy, fairness, and interpretability for binary classification in financial contexts, addressing regulatory and ethical concerns.

Contribution

LDA-XGB1 is a new fair and interpretable XGBoost-based model developed through biobjective optimization, incorporating fairness constraints and interpretability features.

Findings

LDA-XGB1 outperforms traditional models in fairness and accuracy.

The model maintains interpretability with monotonic constraints.

Effective on both simulated and real-world datasets.

Abstract

Fair lending practices and model interpretability are crucial concerns in the financial industry, especially given the increasing use of complex machine learning models. In response to the Consumer Financial Protection Bureau's (CFPB) requirement to protect consumers against unlawful discrimination, we introduce LDA-XGB1, a novel less discriminatory alternative (LDA) machine learning model for fair and interpretable binary classification. LDA-XGB1 is developed through biobjective optimization that balances accuracy and fairness, with both objectives formulated using binning and information value. It leverages the predictive power and computational efficiency of XGBoost while ensuring inherent model interpretability, including the enforcement of monotonic constraints. We evaluate LDA-XGB1 on two datasets: SimuCredit, a simulated credit approval dataset, and COMPAS, a real-world recidivism prediction dataset. Our results demonstrate that LDA-XGB1 achieves an effective balance between predictive accuracy, fairness, and interpretability, often outperforming traditional fair lending models. This approach equips financial institutions with a powerful tool to meet regulatory requirements for fair lending while maintaining the advantages of advanced machine learning techniques.