BiasQD-DT: Creating Interpretable Trade-Offs in Classifier Bias with Quality-Diversity Optimization via Decision Trees
TL;DR
A novel framework that integrates evolved decision trees generated from random seed-based vectors and Quality Diversity (QD) optimization to jointly address decision interpretability, predictive performance, and fairness is proposed.
Abstract
The implementation of Artificial Intelligence (AI) in sensitive domains requires that models are fair and clearly understandable for stakeholders to trust in the decisions made. While Explainable AI (XAI) methods approximate the behavior of models, Interpretable AI (IAI) focuses on inherently transparent representations such as decision trees. However, interpretability alone is not enough, as models may encode biases from different sources, including biases present in data, leading to unfair outcomes. This introduces a fundamental trade-off between predictive performance and fairness. In this paper, we propose a novel framework that integrates evolved decision trees generated from random seed-based vectors and Quality Diversity (QD) optimization to jointly address decision interpretability, predictive performance, and fairness. Using evolutionary search, the method generates a repertoire of high-performing, interpretable models that span different regions of the fairness-accuracy space. This enables a systematic characterization of trade-offs and provides decision-makers with multiple transparent alternatives. Experimental results show that the proposed approach effectively discovers diverse decision tree models with competitive overall accuracy while achieving improved fairness across standard benchmarks.